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This case was judged by an anonymized AI reviewer and has not been independently reviewed by people; inspect the draft, cards, and both outputs to assess it.
AutocompleteWithout cards better227 / 280 · beu-073597f9a6b6f9ca

Machine Learning for Methane Detection and Quantification from Space - A survey

Climate and environmental science · 2408.15122v1

FLOWING EVIDENCE BENCHMARK

How do we compare writing with and without cards?

At the same writing position, the same model produces one first result with cards and one without. All 280 pairs enter the overall anonymized review, which assesses whether a decision not to continue fits the draft.

Overall: 138 with-card wins, 26 ties, 107 no-card wins, 5 pairs where neither was usable, and 4 inconclusive judgments. Both sides wrote text in 218 pairs; 48 had a deliberate stop and 14 included an invalid output.

SAME DRAFT · WITH OR WITHOUT CARDS

01 · FIXED WRITING POSITION

MANUSCRIPT

Same writing position ▌

Matched autocomplete at the same draft position

02 · TWO MATCHED VERSIONS

SHARED BY BOTH

Draft, writing task, and generation model

A · With cards

Relevant literature cards supplied

B · Without cards

No literature cards supplied

Generation model

Same model and version

A → first output

B → first output

Anonymized AI judge

First outputs are anonymized as X and Y

Continuation review: accuracy, fit to the writing task and usability
Returns: X preferred / tie / Y preferred / both unusable

Review again with the answer order swapped

HOW DOES BLIND JUDGING WORK?

① Anonymize both outputs
The AI judge sees the same draft and both first outputs without knowing which received cards.

② Compare and swap order
The judge applies task-specific criteria in X/Y and then Y/X order.

③ Review disagreements
A third pass resolves disagreements. Incomplete reviews remain in the denominator.

How are positions and cards selected?

We selected 40 writing positions in each of seven fields, for 280 comparisons. The position and relevant literature cards were set before each comparison, so both versions began from the same draft.

The main result retains every frozen position, including places where cards might not add a useful next move. This avoids selecting cases based on their outputs, but 49% describes the whole cohort, not a win rate where evidence clearly had an opportunity to help. This cohort did not record that opportunity label for each position before generation, so those two groups cannot yet be separated reliably.

CARD USE · 140/280

Reported card use in continuations with text

The model reports whether a card affected its continuation. In 140 of 280 tests it produced continuation text and reported card use. The previous count of 150 included 10 cases without continuation text and has been corrected.

COMPARISON · 70 / 7 / 30

Which written version was better?

Of the 107 reported-use cases where both versions produced text, the blinded AI judge preferred the with-card version 70 times, found 7 about the same, and preferred the without-card version 30 times.

How are the two table percentages calculated?

ADOPTION COVERAGE

140reported use with text
÷
280all positions
=
50%

WITH-CARD VERSION PREFERRED

70with cards better
÷
107comparable tests
=
65%

The percentages use different groups: 50% counts continuations with text reporting card use among all 280 tests; 65% covers the 107 such cases with text on both sides. The generating model reports card use, while an anonymized AI judge compares writing quality.

Card use is self-reported by the generator. The anonymized AI judge sees the draft and source snippets, so source-aware preference remains possible; there was no independent human review. The overall result describes these 280 frozen positions, not a win probability for any writing task.

Manuscript writing position

Text excerpt · not a PDF page

Research manuscript · excerpt

Machine Learning for Methane Detection and Quantification from Space - A survey

6 Conclusion

…a large training dataset. However, it is unclear if the performance can be generalized. Interestingly, some authors observed a smoothing effect in emission rate estimation in the validation dataset, leading to overestimating low and underestimating high emission rates. This smoothing effect results, among other effects, from strong weights of outliers/extremes of the RMSE function ( Naser and Alavi, 2023 ) . Addressing this issue requires different loss functions or data and a thorough comparison of different deep-learning approaches, which remains relatively unexplored for methane detection.

… data is both expensive and difficult to manage. Ground truth data can either be estimated and compared using different algorithms, as demonstrated in the work by Růžička et al. (2023) , or based on simulations, as explored in the research of Radman et al. (2023) . However, models trained on simulated data need further investigation on larger datasets, as many studies have only tested them on a limited number of samples. One potential solution is to provide precise ground truth through simulations and supplement it with a large real-world test set to evaluate model performance on actual data.

↓ Next paragraph was generated here

The highlighted text is near the writing position. This is a layout of the evaluation input, not a PDF screenshot.

A matched comparison at the same writing positionOverall AI review: Without cards better

Both arms use the same model, task and manuscript context. Only the retrieved passages differ. The first recorded output from each arm is shown below.

Read overall AI review rationale

A = without cards · B = with cards

Output A correctly identifies that the draft is already complete and does not need continuation. The text ends with a clear, substantive point about providing precise ground truth through simulations supplemented with real-world test sets—a coherent concluding thought for this paragraph. Continuing would be redundant or speculative. Output B's 'invalid' action is incorrect here; there's no material contradiction, invented result, or serious topic drift in the draft that would warrant marking it invalid. The 'invalid' action appears to be a misapplication of the action system—this is a valid, complete paragraph that appropriately ends its discussion.

Evidence

Same model and position · retrieved passages supplied · first output

Not preferred in this comparison

No continuation text was produced.

Model action：invalid

Passages supplied to the Evidence version

Card 1 · SeSaMe: A Framework to Simulate Self-Reported Ground Truth for Mental Health Sensing Studies

Advances in mobile and wearable technologies have enabled the potential to passively monitor a person's mental, behavioral, and affective health. These approaches typically rely on longitudinal collection of self-reported outcomes, e.g., depression, stress, and anxiety, to train machine learning (ML…

Read full passage excerpt

Advances in mobile and wearable technologies have enabled the potential to passively monitor a person's mental, behavioral, and affective health. These approaches typically rely on longitudinal collection of self-reported outcomes, e.g., depression, stress, and anxiety, to train machine learning (ML) models. However, the need to continuously self-report adds a significant burden on the participants, often resulting in attrition, missing labels, or insincere responses. In this work, we introduce the Scale Scores Simulation using Mental Models (SeSaMe) framework to alleviate participants' burden in digital mental health studies. By leveraging pre-trained large language models (LLMs), SeSaMe enables the simulation of participants' responses on psychological scales. In SeSaMe, researchers can prompt LLMs with information on participants' internal behavioral dispositions, enabling LLMs to construct mental models of participants to simulate their responses on psychological scales. We demonst …

Card 2 · GPM: A Generic Probabilistic Model to Recover Annotator's Behavior and Ground Truth Labeling

In the big data era, data labeling can be obtained through crowdsourcing. Nevertheless, the obtained labels are generally noisy, unreliable or even adversarial. In this paper, we propose a probabilistic graphical annotation model to infer the underlying ground truth and annotator's behavior. To acco…

Read full passage excerpt

In the big data era, data labeling can be obtained through crowdsourcing. Nevertheless, the obtained labels are generally noisy, unreliable or even adversarial. In this paper, we propose a probabilistic graphical annotation model to infer the underlying ground truth and annotator's behavior. To accommodate both discrete and continuous application scenarios (e.g., classifying scenes vs. rating videos on a Likert scale), the underlying ground truth is considered following a distribution rather than a single value. In this way, the reliable but potentially divergent opinions from "good" annotators can be recovered. The proposed model is able to identify whether an annotator has worked diligently towards the task during the labeling procedure, which could be used for further selection of qualified annotators. Our model has been tested on both simulated data and real-world data, where it always shows superior performance than the other state-of-the-art models in terms of accuracy and robust …

Card 3 · Towards Error Measures which Influence a Learners Inductive Bias to the Ground Truth

Artificial intelligence is applied in a range of sectors, and is relied upon for decisions requiring a high level of trust. For regression methods, trust is increased if they approximate the true input-output relationships and perform accurately outside the bounds of the training data. But often per…

Read full passage excerpt

Artificial intelligence is applied in a range of sectors, and is relied upon for decisions requiring a high level of trust. For regression methods, trust is increased if they approximate the true input-output relationships and perform accurately outside the bounds of the training data. But often performance off-test-set is poor, especially when data is sparse. This is because the conditional average, which in many scenarios is a good approximation of the `ground truth', is only modelled with conventional Minkowski-r error measures when the data set adheres to restrictive assumptions, with many real data sets violating these. To combat this there are several methods that use prior knowledge to approximate the `ground truth'. However, prior knowledge is not always available, and this paper investigates how error measures affect the ability for a regression method to model the `ground truth' in these scenarios. Current error measures are shown to create an unhelpful bias and a new error m …

Card 4 · Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data

Capturing ground truth data to benchmark super-resolution (SR) is challenging. Therefore, current quantitative studies are mainly evaluated on simulated data artificially sampled from ground truth images. We argue that such evaluations overestimate the actual performance of SR methods compared to th…

Read full passage excerpt

Capturing ground truth data to benchmark super-resolution (SR) is challenging. Therefore, current quantitative studies are mainly evaluated on simulated data artificially sampled from ground truth images. We argue that such evaluations overestimate the actual performance of SR methods compared to their behavior on real images. Toward bridging this simulated-to-real gap, we introduce the Super-Resolution Erlangen (SupER) database, the first comprehensive laboratory SR database of all-real acquisitions with pixel-wise ground truth. It consists of more than 80k images of 14 scenes combining different facets: CMOS sensor noise, real sampling at four resolution levels, nine scene motion types, two photometric conditions, and lossy video coding at five levels. As such, the database exceeds existing benchmarks by an order of magnitude in quality and quantity. This paper also benchmarks 19 popular single-image and multi-frame algorithms on our data. The benchmark comprises a quantitative study …

Model-reported card use

Card 4 · fact

Affected output: evaluations on simulated data have been argued to overestimate the performance of methods relative to their behavior on real images, motivating all-real acquisition databases with pixel-wise ground truth

Model explanation: This row supplied the concrete prior-work fact that simulated-data evaluation overestimates performance relative to real images, which the conclusion uses as an explicitly attributed cross-domain parallel.

Card 4 · terminology

Affected output: all-real acquisition databases with pixel-wise ground truth

Model explanation: The row's phrasing for an all-real ground-truth benchmark supplied the field-native terminology used to describe the response to the simulated-to-real gap.

No Evidence

Same model and position · no retrieved passages · first output

Preferred by blind review

No continuation text was produced.

Model action：complete

Earlier card-use comparison

The earlier diagnostic compared only cases with reported card use and text on both sides; its scope differs from the overall AI review above.

The with-card arm returned a card-use record but no continuation text. It is excluded from reported use with text and remains in all 280 first attempts.

The passages are input material; their presence does not mean an output used them or that they support every claim. Draft and source passages are excerpted; no simulated scores or PDF appear here.