Climate and height growth of taiwania (<i>Taiwania cryptomerioides</i>) and Taiwan incense-cedar (<i>Calocedrus formosana</i>) in Taiwan
Bibliographic record
Abstract
Taiwan incense-cedar (Calocedrus formosana (Florin) Florin) and taiwania (Taiwania cryptomerioides Hayata) are coniferous species growing in Taiwan, Republic of China. Both species are red listed, and hence, their management and conservation are important. The objective of this research was to determine the effect of climate on the height growth of Taiwan incense-cedar and taiwania. Height growth data for Taiwan incense-cedar came from stem analysis data and the data for taiwania were from three sets of experimental plots. Climate data were obtained from nearby climate stations. Dynamic height growth models having parameters that were functions of climate variables were fit to the height data. A Chapman–Richards function formed the basis for Taiwan incense-cedar model, and a linear model was fit to the taiwania data. Mean summer temperature and precipitation were predictor variables for Taiwan incense-cedar height growth. These variables, along with growing degree-days, were predictor variables for taiwania. Several scenarios were devised to show the effect of climate on height growth. Under the A1B climate change scenario, the height growth of taiwania is expected to increase, but the height growth of Taiwan incense-cedar will decrease slightly. The climate/height growth relationships can assist in the conservation and stewardship of these species.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".