Estimating maximum mean canopy stomatal conductance for use in models
Bibliographic record
Abstract
Fertilized (F) and irrigated and fertilized (IF) stands of Pinus taeda L. produced twice the leaf area index of irrigated (I) and control (C) stands. Based on sap flux-scaled mean stomatal conductance (GS), we found that stomatal conductance in F was half that in other treatments. During the growing season, GS was related to vapor pressure deficit (D) and soil moisture. During the cooler season, soil moisture was high and light accompanied D in controlling GS. Under all conditions and treatments, the rate of decrease in GS with D was proportional to GS at low D (= 1 kPa). We evaluated whether GS can be used as an input to growth models and used a simple growth model (3-PG), which also predicts stand transpiration (EC), to compare with direct EC measurements in the four stands. Model predictions of monthly EC based on Penman-Montieth equation parameterized with maximum GS (GSmax) estimated under highest "native" soil moisture (0.07 m3·m3) produced long-term values within 10% of measured EC. When the model was parameterized with GSmax estimated under experimentally raised soil moisture, or with porometrically measured conductance, EC values were consistently overpredicted from 12 to 33%. Thus, sap-flux scaled mean canopy stomatal conductance obtained under non limiting light conditions, low D, and highest native soil moisture, is the most appropriate parameter value for certain single-leaf type of models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".