Novel genes are enriched in normalized cDNA libraries from drought-stressed seedlings of rice (<i>Oryza sativa</i> L. subsp. <i>indica </i>cv. Nagina 22)
Bibliographic record
Abstract
We have utilized an efficient method to enrich cDNA libraries for novel genes and genes responsive to drought stress in rice (Oryza sativa L. subsp. indica). We separately constructed standard and normalized cDNA libraries from leaf tissue of rice seedlings grown under controlled drought stress. Sequencing from the 3' end was performed on 1000 clones from the normalized leaf cDNA library and 200 clones from the standard leaf cDNA library. For the first 200 clones, the clone redundancy in the non-normalized library was about 10%, compared with 3.5% in the normalized cDNA library. Comparison of these cDNAs with the sequences in public databases revealed that 28.2% of the expressed sequence tags (ESTs) from the normalized library were novel. Clones from the standard and normalized leaf libraries and a root library uncovered numerous cDNAs that are highly homologous to known drought-responsive genes including those that encode metallothioneins, late embroyonic abundant (LEA) proteins, heat-shock proteins, cytochrome P450 enzymes, catalases, peroxidases, kinases, phosphatases, and transcription factors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".