Methods matter for assessing global variation in plant thermal tolerance
Bibliographic record
Abstract
Lancaster and Humphreys (1) attempted to identify drivers of plant thermal tolerances by analyzing a newly compiled database of heat and cold tolerances. Lancaster and Humphreys conclude that variation in thermal tolerances is attributable to a combination of phylogeny, geography, and local environment, and that the observed patterns “are not an artifact of measurement method” used to estimate tolerances. We applaud Lancaster and Humphreys’ efforts to compile and analyze global patterns in thermal tolerances, but we feel that it is necessary to highlight additional sources of methodological variation that could alter the authors’ analyses and conclusions about plant thermal tolerance macrophysiology. Here, we focus on heat tolerances included … [↵][1]1To whom correspondence may be addressed. Email: t.more.perez{at}gmail.com. [1]: #xref-corresp-1-1
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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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".