Evaluating Douglas-fir and western hemlock volume growth in response to thinning and fertilisation
Bibliographic record
Abstract
Thinning and fertilisation are two silvicultural tools which can modify the growth of a stand. Thinning re-allocates the resources on a site to increase the growth of the trees remaining after the thinning but does not necessarily increase total stand volume as compared to an unthinned stand. Fertilisation is intended to increase the growth of all trees in a stand resulting in more volume. Understanding the response of fertilisation and thinning treatments is critical to making good silviculture prescriptions. To assist with making these prescriptions, yield models for Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco var. menziesii ) and western hemlock ( Tsuga heterophylla (Raf.) Sarg.) on coastal sites of British Columbia, Canada were developed. Douglas-fir and western hemlock are the two most important species on these sites. The data for the modelling came from a large fertilisation and thinning trial (EP703). The model for total volume is based on the Chapman-Richards function. One parameter of the model was expressed as a function of a previous measurement and the other two parameters, which are in turn expressed as a linear function of site index and thinning and/or fertilisation intensity. Independent models were fitted for both species in the study using maximum likelihood estimation. The models were programmed into a spreadsheet to evaluate the behaviour of the models and examine selected responses. These growth and yield models for Douglas-fir and western hemlock allow forest practitioners to evaluate the outcomes of proposed silviculture prescriptions.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".