Modeling net CO<sub>2</sub> assimilation (<i>A</i><sub>N</sub>) within the crown of young planted <i>Larix olgensis</i> trees
Bibliographic record
Abstract
Net CO2 assimilation (AN) is an important physiological indicator that reflects the photosynthetic capacity. The seasonal and spatial variations of AN play an important role in carbon uptake simulations, especially for trees. To gain a clearer understanding of the state of the branch carbon balance, it is necessary to more carefully evaluate the dynamic variation of AN over different gradients in the crown during the growing season. Gas exchange, leaf temperature (Tleaf), vapor pressure deficit (VPD), leaf mass per area (LMA), and relative depth into crown (RDINC) were measured throughout the growing season of planted Larix olgensis A. Henry trees. A semi-empirical model for predicting multilayered crown AN was established by incorporating Tleaf, VPD, LMA, RDINC, and their combinations into a photosynthetic light response (PLR) curve model using re-parameterization. The model was assessed based on goodness of fit (adjusted coefficient of determination ([Formula: see text]), root mean square error (RMSE), and Akaike’s information criterion (AIC)) and on the validation results (mean error (ME), mean absolute error (MAE), precision estimation (P)) and performed well. The multilayered predicted model of crown AN lays the foundation for calculating the multilayered photosynthetic production within the crown and determining the range of the functional crown for individual trees.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".