Intraspecific Variation in Chlorophyll Fluorescence and Pigment Composition in White Spruce (Picea glauca): New Physiological Traits for Identifying Trees Better Adapted to New Climates
Bibliographic record
Abstract
Analysis of intraspecific variation in photosynthetic productivity and phenology can help us to better understand local adaptation and inform breeding decisions for optimally performing genotypes in extreme and changing environments. We used chlorophyll fluorescence, pigment composition, ground and drone-based spectral reflectance measurements to quantify the variation in photosynthesis of ten 5-year-old white spruce genotypes across a latitudinal gradient in summer, and within one site over a year to determine differences between the ten genotypes in seasonal changes of photosynthesis. The results reveal no genotype-environment interactions or differences between genotypes for pigment composition or fluorescence parameters likely due to a lack of stressful events. However one genotype performed consistently across environments, potentially having stable higher photoprotective capacity than other genotypes. The strong relationship found between vegetation indices measured from the drone and ground based physiological data will enable the high-throughput monitoring of the phenology of photosynthesis in conifer forests.
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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 | 0.000 | 0.000 |
| 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".