Light-energy processing and freezing-tolerance traits in red spruce and black spruce: species and seed-source variation
Bibliographic record
Abstract
Red spruce (Picea rubens Sarg.) and black spruce (Picea mariana (Mill.) B.S.P.) are genetically and morphologically similar but ecologically distinct species. We determined intraspecific seed-source and interspecific variation of red spruce and black spruce, from across the near-northern margins of their ranges, for several light-energy processing and freezing-tolerance adaptive traits. Before exposure to low temperature, red spruce had variable fluorescence (Fv) similar to black spruce, but higher photochemical efficiency (Fv/Fm), lower quantum yield, lower chlorophyll fluorescence (%), and higher thermal dissipation efficiency (qN), although the seed-source effect and the seed-source x species interaction were significant only for Fv/Fm. After low-temperature exposure (-40 degrees C), red spruce had significantly lower Fv/Fm, quantum yield and qN than black spruce, but higher chlorophyll fluorescence and relative fluorescence. Species, seed-source effect, and seed-source x species interaction were consistent with predictions based on genetic (e.g., geographic) origins. Multi-temperature exposures (5, -20 and -40 degrees C) often produced significant species and temperature effects, and species x temperature interactions as a result of species-specific responses to temperature exposures. The inherent physiological species-specific adaptations of red spruce and black spruce were largely consistent with a shade-tolerant, late-successional species and an early successional species, respectively. Species differences in physiological adaptations conform to a biological trade-off, probably as a result of natural selection pressure in response to light availability and prevailing temperature gradients.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".