Climate Data and Analytics for Maize Phenotype Predictability and Uncertainty Assessment
Bibliographic record
Abstract
This dissertation aims to develop and implement a climate-analytics framework to improve maize yield predictability. The statistical genetic-by-environment (GxE) model is applied to identify how hydroclimate interacts with maize genetics' molecular markers through covariance matrix structure to improve the predictability of and propagate the uncertainty to maize yield simulations. A multi-year, multi-environment, and multi-dimensional database across the U.S. and Ontario in Canada is obtained from the Genomes to Field (G2F) initiative and preprocessed for the GxE simulations and the proposed analytical framework. The G2F environmental dataset (including temperature, dew point, relative humidity, solar radiation, rainfall, wind speed, wind direction, and wind gust time series during the maize growing season) is improved using deep learning analytics to fulfill the missing values. The improved environmental, genetic, and phenotypic data (OMICs) are integrated into the GxE-based prediction, and the skill enhancement is examined. The GxE performance applies three trial selection schemes (i.e., "random-based," "covariance-based," and "climate-based") to evidence how the hydroclimate variability contributes to the GxE model performance. The sensitivity of the GxE performance to the environmental covariance matrix constructed by hydroclimate drivers is analyzed by employing a global sensitivity analysis (GSA) method called PAWN. The quality and consistency control algorithms for consolidating a homogeneous and multi-dimensional database consisting of improved hydroclimate time series, OMICs, and metadata have been designed for crop phenotypes prediction following the FAIR principles. We can conclude that the proposed analytical frameworks improve GxE performance by enhancing the hydroclimate time series used by GxE simulations. The proposed GSA-GxE framework quantifies the sensitivity of GxE model performance to the covariant climate and molecular genetic markers, identifying the primary sources of uncertainty in phenotype predictability. A quality and consistency control preprocessing algorithms were developed for a multi-dimensional database consisting of hydroclimate time series, OMICs datasets, and metafile as the input of the GxE model. The homogeneous, integrated, and improved multi-dimensional database is released for other researchers interested in maize phenotypic predictability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".