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 (AIC c ) 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 R 2 = 0.65. By comparison, the ΔT model involving only mean July temperatures was less successful (R 2 = 0.49). There was good regional synchrony (r p = 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".