The relationship between tree diameter growth and climate for coniferous species in northern California
Bibliographic record
Abstract
The difference between actual and predicted growth rates for the conifer regions of northern California has been observed to vary with climatic changes. This study presents a method to investigate the relationship between growth and climate. Growth variations attributable to biological and cultural factors were removed by using the CACTOS (California conifer timber output simulator) program. The remaining variation was then associated with relative precipitation and temperature for the projected period and the CACTOS calibration period. Climatic data from the current and preceding years were considered. Elevation, stand density, and species were also investigated to determine their effects on the format and magnitude of the relationship between growth and climate. The results of this study, which included tests of stem analysis data taken over 15 years, indicate that growth variation is associated with the climatic changes of winter precipitation and summer temperatures for the region, in addition to biological and cultural factors. Winter precipitation and summer temperatures affect growth in the current and the subsequent years. Moreover, the relationship between climate and growth changes by densities and species. This study provides a basis for using short-term growth data to make long-term growth projections with growth adjusted to long-term climatic conditions.
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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 | 0.000 | 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) | 0.001 | 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".