General response functions to silvicultural treatments in loblolly pine plantations
Bibliographic record
Abstract
Forest growth and yield models that incorporate common silvicultural practices are essential for practicing intensive forest management. We present models for growth response to a wide range of silvicultural treatments in loblolly pine (Pinus taeda L.) plantations. Baseline models for basal area, dominant height, and survival were fitted using data from across the southeastern United States. Growth models for treated stands were developed by multiplying baseline models with modifier response functions accounting for effects of thinning, fertilization, and control of competing vegetation. Response to early control of competing vegetation was incorporated into baseline models through multiplier factors that were calculated from growth differences between treated and untreated stands. The thinning response function included duration and rate parameters and was sensitive to stand age at the time of thinning, time since thinning, and intensity of thinning. Fertilization response functions were based on the Weibull distribution; the magnitude of responses varies with time since application of fertilizers, type of fertilizer, and rate of application. A difference function, derived from a differential equation with age, initial stand density, and site index as predictors, served as the baseline survival model. The survival model was adjusted for thinning treatment by including an additional independent variable that represents thinning intensity. The resulting models were able to predict growth response when single, as well as multiple, treatments were applied.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".