Analysis of ESTs from multiple <i>Gossypium hirsutum</i> tissues and identification of SSRs
Bibliographic record
Abstract
In an effort to expand the Gossypium hirsutum L. (cotton) expressed sequence tag (EST) database, ESTs representing a variety of tissues and treatments were sequenced. Assembly of these sequences with ESTs already in the EST database (dbEST, GenBank) identified 9675 cotton sequences not present in GenBank. Statistical analysis of a subset of these ESTs identified genes likely differentially expressed in stems, cotyledons, and drought-stressed tissues. Annotation of the differentially expressed cDNAs tentatively identified genes involved in lignin metabolism, starch biosynthesis and stress response, consistent with pathways likely to be active in the tissues under investigation. Simple sequence repeats (SSRs) were identified among these ESTs, and an inexpensive method was developed to screen genomic DNA for the presence of these SSRs. At least 69 SSRs potentially useful in mapping were identified. Selected amplified SSRs were isolated and sequenced. The sequences corresponded to the EST containing the SSRs, confirming that these SSRs will potentially map the gene represented by the EST. The ESTs containing SSRs were annotated to help identify the genes that may be mapped using these markers.
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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) | 0.001 | 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".