Bibliographic record
Abstract
The interaction between genomics and quantitative genetics has been a two-way street. Genomics contributed genetic markers and genetic maps making it possible to study quantitative trait loci (QTLs), and quantitative genetics contributed new theories and computational techniques to deal with the data generated by QTL studies. QTL studies in forest trees have led to the discovery of a few major genes masquerading as quantitative genes, such as genes for rust resistance in several pine species. QTLs for many traits including height growth, leaf traits, wood specific gravity, flowering, frost resistance, disease resistance, and ease of vegetative propagation were found in one or more species. Spring cold hardiness in Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) holds the record for number of QTLs with 14. Generally the number is under seven. The effects are often large, but this may often be due to small population sizes. At this time the impact on forest tree breeding is small, although the potential is certainly there. An interesting marker aided back-crossing program is underway in American chestnut (Castanea dentata (Marsh.) Borkh.).
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".