Modeling mortality in mixed-species stands of coastal British Columbia
Bibliographic record
Abstract
Individual-tree distance-independent models were developed to estimate regular mortality for western hemlock ( Tsuga heterophylla (Raf.) Sarg.), Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco var. menziesii), and western redcedar ( Thuja plicata Donn ex D. Don) in the coastal temperate rain forests of British Columbia, Canada. Permanent plots remeasured at intervals ranging from 1 to 17 years were used. Because of the irregular remeasurement intervals, survival was estimated using a generalized logistic model and mortality was calculated by subtraction. Basal area of trees larger than the subject tree provided reasonably accurate mortality estimates for larger trees. However, poor results were obtained for trees less than 7.5 cm in diameter at breast height, which had higher mortality rates than the larger trees. Since the implementation of a survival (or mortality) model within a growth and yield model environment can largely affect estimation accuracy, three methods of implementing the model were also evaluated. A probability multiplier approach where the stems per hectare surviving to the next period is estimated by multiplying the probability of survival by the stems per hectare at the beginning of the time period is recommended. This is equivalent to a stochastic approach averaged over many repetitions but with much less processing time.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".