Selection of white spruce families in the context of climate change: heat tolerance
Bibliographic record
Abstract
To assess the responses and plasticity of white spruce seedlings (Picea glauca (Moench) Voss) to high temperatures, 12 open-pollinated families differing in growth performance were exposed to a 30-min heat treatment of 42, 44, 46, 48, or 50 degrees C with or without heat preconditioning at 38 degrees C for 5 h. Damage was evaluated based on chlorophyll fluorescence parameters after heat preconditioning, after the heat treatments and during a 7-day recovery period. Visible needle damage was also evaluated after the heat treatments and 14 days later. Chlorophyll fluorescence parameters indicated that seedlings subjected to a heat treatment of 42-43 degrees C lost the ability to phosphorylate and donate water to photosystem II (PSII). A heat treatment of 44-46 degrees C severely limited the ability of the seedlings to use NADPH and ATP in the Calvin cycle. Based on visible needle damage, families with superior height-growth performance were more sensitive to heat stress than families with intermediate or inferior height-growth performance. Moreover, families with superior height-growth performance had low photochemical efficiencies in the light (DeltaF/F(m)') after heat treatment. Heat preconditioning increased the thermotolerance of the seedlings. However, the data suggest that white spruce seedlings exhibiting fast-growing characteristics under present conditions may not grow as well at higher temperatures.
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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) | 0.001 | 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".