Predicting white spruce cone crops in the boreal forests of southern and central Yukon
Bibliographic record
Abstract
White spruce (Picea glauca (Moench) Voss) cone crops were measured at five regional centers in southern and central Yukon for 30 years at one site from 1986 to 2015 and at four other sites during 9 to 11 years to select the best climatic model that uses cues from growing season temperature and rainfall to predict the size of cone crops. We evaluated six climatic models that use summer temperature and rainfall of years t – 1 and t – 2 to predict cone crops in year t. July temperatures provided the best predictors of white spruce cone crops, and no rainfall variable was related to the size of cone crops. We explored three variants of July temperatures: mean temperature, degree-days > 5 °C, and maximum temperatures. For each of these, we used the ΔT model that uses the difference in the July temperature measures of years t – 1 and t – 2. We compared the resulting six models with corrected Akaike’s information criterion (AICc) to determine their relative predictive performance. The best model combined ΔT measures of degree-days > 5 °C and the four highest daily maximum July temperatures with R2 = 0.65. By comparison, the ΔT model involving only mean July temperatures was less successful (R2 = 0.49). There was good regional synchrony (rp = 0.7 to 0.8) in high cone crops over southern and central Yukon during 1986 to 2015.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".