Trade‐offs between net assimilation rate and specific leaf area in determining relative growth rate: relationship with daily irradiance
Bibliographic record
Abstract
Summary Three separate experiments were conducted, involving 27 herbaceous species and 14 woody species aged 15–30 days, in order to determine the relative importance of net assimilation rate (NAR), specific leaf area (SLA) and leaf weight ratio (LWR) in explaining interspecific variations in relative growth rate. Daily quantum inputs were 31·12 mol m−2 day−1 in the first experiment and 33·17 mol m−2 day−1 in the second and third experiments. This is about twice the typical irradiance of most other experiments in this area, but only about 85% of the daily photon flux in nature. Plants were cultivated in hydroponic sand culture in a solution containing 5·8 mm nitrogen. RGR was strongly and positively correlated with NAR in all three experiments. RGR was weakly and negatively correlated with SLA, while the correlation between RGR and LWR was weak and variable. These results are compared to those already published in the literature: the commonly reported result that interspecific variation in RGR is determined primarily by SLA is partly due to the low irradiance used in most experiments, and the relative importance of SLA and NAR changes depending on irradiance. A hypothesis is proposed in which direct and indirect effects of SLA on each of NAR and RGR are decomposed, and which leads to a trade‐off between SLA and NAR as a function of daily irradiance.
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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 | 0.001 | 0.001 |
| 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".