636 candidates reviewed from the core sample

Preliminary results

The current AI review suggests that basic study goals are usually reported, while computational reproducibility, uncertainty, and external validation are much less consistent. Human validation has not started, so these figures describe the reviewed subset rather than the literature.

636lexical candidates screened
189eligible full ML-based-science papers
49.1%applicable items fully reported
0human validation checks completed

49.1% of applicable items were fully reported

Each of the 189 eligible papers was assessed against 32 reporting items derived from Princeton's REFORMS. Of 5,913 applicable item judgments, 2,904 were fully reported, 1,667 were partly reported, and 1,342 were absent or unclear. The secondary score gives half credit to partial reporting.

The 49.1% figure is an item-level reporting rate; papers were not assigned pass/fail results. Every eligible paper fully reported at least 2 items.

189 candidates met the full scoring criteria

The candidate frame includes papers whose titles or abstracts use explicit ML language. Screening then asks whether model performance or output is used as evidence for a scientific claim, and whether a complete original paper is available.

Eligibility outcome

OutcomePapersShare
Eligible full ML-based-science papers18929.7%
Not ML-based science43568.4%
ML-based science, but no complete paper body81.3%
Unresolved40.6%

Scientific-use classification

ClassPapersShare
ML-based science19731.0%
ML methods research14122.2%
ML mentioned, but not used as evidence12619.8%
Predictive analytics10516.5%
Non-ML quantitative analysis629.7%
Unresolved50.8%

The 197 ML-based-science candidates comprise 189 complete original papers and 8 records without a complete paper body, which could not be scored against the full reporting standard.

Publication form in the candidate set

Publication form is recorded separately from scientific use. This keeps reviews, editorials, responses, and conference abstracts visible as sample outcomes without treating them as complete empirical papers.

Publication formPapersShare
Original Research48876.7%
Review639.9%
Systematic Review193.0%
Editorial Opinion182.8%
Conference Abstract162.5%
Other121.9%
Bibliometric Review71.1%
Commentary Response60.9%
Meta Analysis30.5%
Protocol30.5%
Uncertain10.2%

Pre/post comparison

The currently eligible papers include 90 before REFORMS and 99 after it. Full reporting was 49.0% before and 49.2% after, a change of +0.2 percentage points. The half-credit score changed by -0.5 points. These figures remain descriptive: the comparison contains 189 papers, and human validation has not started.

PeriodPapersApplicable itemsPresentPartialAbsent or unclearFull reportingHalf-credit score
Before REFORMS902,8151,37981462249.0%63.4%
After REFORMS993,0981,52585372049.2%63.0%

Paper-level reporting varied substantially

Every eligible paper reported at least some REFORMS-aligned information. The table treats each paper equally; the preceding comparison treats each applicable item equally.

PeriodPapersMean paper scoreMedianRangePapers with zero present items
Before REFORMS9049.0%48.4%9.7% to 93.8%0
After REFORMS9949.2%50.0%6.2% to 90.3%0

Run instructions and reproduction workflows were rarely reported

Across the 189 papers, run instructions and end-to-end reproduction workflows were almost never supplied. Code versions, missing-data frequencies, uncertainty estimates, external validity, exclusions, and safeguards against leakage were also often absent or incomplete.

ItemReporting requirementPresentPartialAbsentFull reporting
R2dREADME or equivalent run instructions supplied181800.5%
R2eReproduction script or workflow supplied12281496.3%
R2bCode and code version identified7401423.7%
R3fMissing data frequency reported15451228.2%
R6bDependencies or duplicates across train/test partitions addressed213711811.9%
R7bUncertainty estimates reported with calculation method404310621.2%
R4bImpossible or corrupt sample handling described49498826.3%
R8aExternal validity evidence reported39678320.6%
R6aPreprocessing and modeling restricted to training information where required33726918.8%
R4aSample exclusions identified and justified77535741.2%
R5eHyperparameter tuning reported52884228.6%
R5fBaseline comparisons justified96514250.8%

Shown: the 12 items with the largest absent counts. The downloadable item table contains all 32 items, including unclear and not-applicable counts.

Study goals were reported more consistently than reproducibility details

Module-level scores show the contrast. Authors generally explained their scientific goals and models, but often did not provide the artifacts needed to rerun the analysis or the evidence needed to judge transportability.

ModulePresentPartialAbsentFull reportingHalf-credit score
Computational Reproducibility10131552810.7%27.4%
Data Leakage15918919129.3%46.8%
Metrics Uncertainty19418111339.8%58.3%
Generalizability Limitations189979250.0%62.8%
Data Preprocessing28213214850.2%61.9%
Data Quality74541315556.7%72.5%
Modeling70130711162.6%76.4%
Study Goals53333194.0%96.9%

Explicit REFORMS adoption

None of the 189 eligible papers cited REFORMS, and none supplied a REFORMS checklist. That is unsurprising for a small early sample: publication lags, indirect adoption through journal policy, and broader reproducibility norms can all affect reporting without producing a citation.

Human validation has not started

Codex performed the screening and item-level assessment from the retrieved paper texts. Human validation checks eligibility decisions and individual item scores so that the project can estimate AI error and place uncertainty bounds around later results. The current 636 screening decisions come from a frozen 1,200-candidate sample.

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