Modification of a photosynthetic light-response (PLR) model for modeling the vertical gradient in the response of crown PLR curves
Bibliographic record
Abstract
The photosynthetic light-response (PLR) curve is a mathematical description of a single biochemical process that has been widely applied in many ecophysiological models. For trees, the heterogeneity of PLR curves within the crown is significant but rarely modeled by mathematical techniques. This paper establishes a modified model for estimating crown PLR curves based on PLR functions by linking the parameters of the PLR functions to leaf nitrogen (N), specific leaf area (SLA), and relative depth into the crown (RDINC). The modified models were assessed by considering the goodness of fit (adjusted coefficient of determination, [Formula: see text]; root mean square error, RMSE; and Akaike information criterion, AIC) and model structure. Significant correlations were observed between the parameters of PLR functions and N, SLA, and RDINC. The optimal modified PLR model, by linking RDINC into a modified Mitscherlich function, fit well due to its simple and easily understood structure. Therefore, it is feasible to simultaneously estimate the multilayered and varied PLR curves of the tree crown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".