Net assimilation rate, specific leaf area and leaf mass ratio: which is most closely correlated with relative growth rate? A meta‐analysis
Bibliographic record
Abstract
Summary Data were compiled consisting of 1240 observations (614 species) from 83 different experiments published in 37 different studies, in order to quantify the relative importance of net assimilation rate (NAR, g cm −2 day −1 ), specific leaf area (SLA, cm 2 g −1 ) and leaf mass ratio (LMR, g g −1 ) in determining relative growth rate (RGR, g g −1 day −1 ), and how these change with respect to daily quantum input (DQI, moles m −2 day −1 ) and growth form (herbaceous or woody). Each of ln(NAR), ln(SLA) and ln(LMR) were separately regressed on ln(RGR) using mixed model regressions in order to partition the between‐experiment and within‐experiment variation in slopes and intercepts. DQI and plant type were then added to these models to see if they could explain some of the between‐experiment variation in the relative importance of each growth component. LMR was never strongly related to RGR. In general, NAR was the best general predictor of variation in RGR. However, for determining RGR the importance of NAR decreased, and the importance of SLA increased, with decreasing daily quantum input in experiments containing herbaceous species. This did not occur in experiments involving woody species.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.022 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".