Density-dependent mortality in<i>Taiwania cryptomerioides</i>and<i>Chamaecyparis formosensis</i>stands in Taiwan
Bibliographic record
Abstract
Taiwania (Taiwania cryptomerioides Hayata) and red cypress (Chamaecyparis formosensis (Matsum.)) are two red-listed species found mostly in Taiwan. A better understanding of the mortality patterns is necessary for good forest management of taiwania and red cypress, which is critical given that they are vulnerable and threatened species, respectively. The data for this project come from thinning trials where high-density plantations were established and later thinned. Mortality due to thinning was not included in the analysis. The mortality data were fitted to an exponential function using a negative binomial distribution model under a finite mixed modeling framework with stand density measures as predictor variables. The negative binomial distribution was zero-inflated for red cypress. Maximum mortality rates were fitted to the same exponential function used to model the mean response. Generally, average and maximum mortality rates increased as stand density increased, with stocking and average tree basal area having a large influence on mortality. Mortality rates were higher for red cypress than for taiwania. The differences in mortality rates could be due to the species relative shade tolerance or their ability to withstand competition. The mortality models can be used for developing thinning prescriptions and managing these species for conservation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".