CLImate for Maize OMICS: CLIM4OMICS Analytics and Database
Bibliographic record
Abstract
CLIM4OMICS Analytics and Database is Improved database of G2F data repository that contains OMICs (genetic and phenotypic) and environmental data for maize yield predictability across 84 experimental fields in the U.S. and province of ON in Canada between 2014-2017. The goal of this pipeline is to aggregate, improve, and synthesize multi-dimensional G2F data including Geno-type, Phenotype and Environmental data for GxE modeling. This dataset contains 8,171 phenotype measurements, 376 genotypes of maize lines, environmental data of 84 locations and Python Scripts for Quality control (QC), Consistency control (CC) steps and ML models for GxE interactions. The Environmental data is extracted from NWS, DayMet and NSRDB databases and processed for QC and CC. The environmental dataset contains the minimum temperature (Tmin), average temperature (Tmean), maximum temperature (Tmax), minimum dew point (DPmin), average dew point (DPmean), maximum dew point (DPmax), minimum relative humidity (RHmin), average relative humidity (RHmean), maximum relative humidity (RHmax), minimum solar radiation (SRmin), average solar radiation (SRmean), maximum solar radiation (SRmax), accumulative rainfall (Racc), average wind speed (WSmean), and average wind direction (WDmean). This package also contains the raw G2F data and preprocessing pipeline.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.018 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".