Plant heat tolerance characterizes carbon assimilation strategy
Bibliographic record
Abstract
The heat tolerance of photosystem II (PSII) may promote carbon assimilation at higher temperatures and may help explain plant responses to climate change. PSII heat tolerance could lead to 1) increases in the high temperature compensation point (Tmax); 2) increases in the thermal breadth of photosynthesis (i.e. the photosynthetic Ω parameter) to promote a thermal generalist strategy of carbon assimilation; 3) increases in the optimum rate of carbon assimilation Popt and promote faster carbon assimilation; and/or 4) increases in the optimum temperature for photosynthesis (Topt). To address these hypotheses, we tested if the Tcrit, T50 and T95 metrics of PSII heat tolerance were correlated with each carbon assimilation parameter for 21 species. Hypothesis 1 was not supported, but we observed that T50 may estimate the upper thermal limit for Tmax at the species-level, and that community mean Tcrit may be useful for approximating Tmax. The T50 and T95 heat tolerance metrics were positively correlated with Ω in support of hypothesis 2. We found no support for hypotheses 3 or 4. Our study shows that high PSII heat tolerance is unlikely to improve carbon assimilation at higher temperatures, but may characterize thermal generalists with slow resource acquisition strategies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".