Peer Review #2 of "Long-term warming results in species-specific shifts in seed mass in alpine communities (v0.1)"
Bibliographic record
Abstract
Background.Global warming can cause variation in plant functional traits due to phenotypic plasticity or rapid microevolutionary change.Seed mass represents a fundamental axis of trait variation in plants, from an individual to a community scale.Here, we hypothesize that long-term warming can shift the mean seed mass of species.Methods.We tested our hypothesis in plots that had been warmed over 18 years in alpine meadow communities with a history of light grazing (LG) and heavy grazing (HG) on the Qinghai-Tibet plateau.In this study, seeds were collected during the growing season of 2015.Results.We found that warming increased the mean seed mass of 4 (n=19) species in the LG meadow and 6 (n=20) species in the HG meadow, while decreasing the mean seed mass of 6 species in the LG and HG meadows, respectively.For 7 species, grazing history modified the effect of warming on seed mass.Therefore, we concluded that long-term warming can shift the mean seed mass at the species level.However, the direction of this variation is species-specific.Our study suggests that mean seed mass of alpine plant species appears to decrease in warmer (less stressful) habitats based on lifehistory theory, but it also suggests there may be an underlying trade-off in which mean seed mass may increase due to greater thermal energy inputs into seed development.Furthermore, the physical and biotic environment modulating this trade-off result in complex patterns of variation in mean seed mass of alpine plant species facing global warming.
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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 | 0.012 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.361 | 0.222 |
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".