Climatic determinants of white spruce cone crops in the boreal forest of southwestern Yukon
Bibliographic record
Abstract
White spruce ( Picea glauca (Moench) Voss) cone crops were measured from 1986 to 2011 in the Kluane region of southwestern Yukon to test the hypothesis that the size of cone crops could be predicted from spring and summer temperature and rainfall of years t, t – 1, and t – 2. We counted cones in the top 3 m of an average of 700 white spruce trees each year spread over 3–14 sites along 210 km of the Alaska Highway and the Haines Highway. We tested the conventional explanation for white spruce cone crops that implicates summer temperatures and rainfall in years t and t – 1 and rejected it, since it explained very little of the variation in our 26 years of data. We used exploratory data analysis with robust multiple regressions coupled with Akaike’s information criterion corrected (AICc) analysis to determine the best statistical model to predict the size of cone crops. We could statistically explain 54% of the variation in cone crops from July and August temperatures of years t – 1 and t – 2 and May precipitation of year t – 2. There was no indication of a periodicity in cone crops, and years of large cone crops were synchronous over the Kluane region with few exceptions. This is the first quantitative model developed for the prediction of white spruce cone crops in the Canadian boreal forest and has the surprising result that weather conditions 2 years prior to the cone crop are the most significant predictors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".