Time vs. light: a potentially useable light sum hybrid model to represent the juvenile growth of Douglas-fir subject to varying levels of competition
Bibliographic record
Abstract
Substitution of potential useable light sum for time in a commonly used mensurational equation resulted in a better fit to data from a complex vegetation management experiment. The experiment involved Douglas-fir ( Pseudotsuga menzeisii (Mirb.) Franco) as a crop species and a variety of competing species. Site occupancy by competing vegetation varied with time because control operations were intermittently either included or excluded from treatments over a period of 4 years. There were four randomized complete blocks of eight competition control treatments. Potentially useable light sum was estimated using measurements of radiation from a meteorological station that were modified by coefficients representing the ability of the crop plants to use light with varying soil water, vapour pressure deficit, and temperature. Light sums were further reduced by estimated competition for light from competing vegetation. Fits of the model to individual plots within the experiment yielded coefficients that did not differ significantly between competition control treatments, suggesting that the model accounted for significant variations in growth resource availability between treatments. Potentially useable light sum equations provide an integrated link between traditional mensurational modeling and ecophysiological modeling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".