Comprehensive developmental profiles of gene activity in regions and subregions of the <i>Arabidopsis</i> seed
Bibliographic record
Abstract
Seeds are complex structures that consist of the embryo, endosperm, and seed-coat regions that are of different ontogenetic origins, and each region can be further divided into morphologically distinct subregions. Despite the importance of seeds for food, fiber, and fuel globally, little is known of the cellular processes that characterize each subregion or how these processes are integrated to permit the coordinated development of the seed. We profiled gene activity genome-wide in every organ, tissue, and cell type of Arabidopsis seeds from fertilization through maturity. The resulting mRNA datasets offer the most comprehensive description of gene activity in seeds with high spatial and temporal resolution,providing unique insights into the function of understudied seed regions. Global comparisons of mRNA populations reveal unexpected overlaps in the functional identities of seed subregions. Analyses of coexpressed gene sets suggest that processes that regulate seed size and filling are coordinated across several subregions. Predictions of gene regulatory networks based on the association of transcription factors with enriched DNA sequence motifs upstream of coexpressed genes identify regulators of seed development. These studies emphasize the utility of these data sets as an essential resource for the study of seed biology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".