Characterizing the frost sensitivity of black spruce photosynthesis during cold acclimation
Bibliographic record
Abstract
We used photosynthetic light response curves to measure and model the responses of two provenances of 3-year-old black spruce (Picea mariana (Mill.) BSP) seedlings to severe artificial frost treatments applied at 2-week intervals during cold acclimation. Black spruce seedlings responded to cold acclimation with long-term suppression of photosynthetic capacity (Amax) and apparent quantum-use efficiency (alpha'). Short-term reductions in both photosynthetic parameters following frost treatments were dependent on the extent of cold acclimation of the seedlings and the severity of the frost treatments. Large reductions in Amax in response to the frost treatments were observed in seedlings that had undergone little cold acclimation and these reductions were associated with an irreversible reduction in alpha'. Such seedlings recovered only partially during the subsequent 23 days, whereas seedlings in most other treatments showed complete recovery of Amax after 13 days. The impact of frost treatments on Amax and alpha' did not vary with seedling provenance. We propose an algorithm that predicts the combined effects of cold acclimation and severe freezing temperatures on the extent of the suppression of A(max) during autumn. The algorithm is based on (1) the maximum Amax observed during the growing season, (2) the accumulation of cold degree-days, based on a minimum nocturnal temperature < 5 degrees C, and (3) the severity of freezing temperatures during autumn. The parameters developed in the algorithm showed that cold acclimation of black spruce seedlings had a greater impact on the reduction of Amax in autumn than did the severe frost treatments. Mean Amax of seedlings subjected to artificial frosts showed a strong correlation with values predicted by the algorithm (r2 = 0.91).
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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 | 0.001 |
| 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".