Different cone colours pay off: lessons learnt from European larch (<i>Larix decidua</i>) and Norway spruce (<i>Picea abies</i>)
Bibliographic record
Abstract
The colour morphs of immature female cones in European larch (Larix decidua Mill.) and Norway spruce (Picea abies (L.) Karst.) are green, red, and intermediate in colour. For the first time, these three colour morphs were studied to verify the thermoregulatory hypothesis and to investigate its underlying genetic spatial pattern. The study was based on an extensive systematic sampling, and data were analysed using principal component analysis (PCA), binary logistic regression (BLR), and spatial autocorrelation. Correlations between the nontransformed environmental variables and PC scores revealed two main ecological gradients, (i) altitude–temperate and (ii) annual precipitation. Loadings of the first two principal components exceeded 85% in both species. BLR was used to test the effect of the altitude–temperature gradient on the probability of occurrence of a specific cone colour. In both species, the occurrence of red cones was significantly positively related to high altitude with low temperatures, while green cones were significantly negatively correlated with decreasing temperature and increasing altitude. In both species the spatial pattern based on a putative Mendelian gene was nonrandom as indicated by significantly high Moran’s I values based on altitudinal distance. Spatial genetic structure was probably maintained by limited gene flow and balanced selection that maintained short-distance genetic differentiation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".