Climatic determinants of berry crops in the boreal forest of the southwestern Yukon
Bibliographic record
Abstract
Berry crops in the southwestern Yukon were quantified from 1997 to 2008 at 10 locations along 210 km of the Alaska and Haines highways. We tested the hypothesis that the size of berry crops could be predicted from spring and summer temperature and rainfall of years t, t–1 (1 year prior), and t–2 (2 years prior). Six common species were studied in the boreal forest of the Kluane region: Arctostaphylos rubra (Rehd. & Wils.) Fern., Arctostaphylos uva-ursi (L.) Spreng. s.l., Empetrum nigrum L., Vaccinium vitis-idaea L., Geocaulon lividum (Richards) Fern, and Shepherdia canadensis (L.) Nutt.. For the first five species we counted berries on fixed 40 cm × 40 cm quadrats to obtain an index of berry production for the Kluane region for each of the 12 years, and for S. canadensis we counted berries on two tagged branches of 10 bushes at each location. Stepwise multiple regressions were utilized to predict the size of berry crops for each species. For all species, predictive equations could explain statistically 80%–96% of the variation in berry crops. Different weather variables characterized each plant species, and there was no common weather regression that could explain the variation in berry crops in all species. Rainfall and temperature from years t–1 and t–2 were typically the significant predictors. There was no indication of a periodicity in berry production, and 43%–60% of the quadrats counted had large berry crops at one year intervals, while other quadrats never had a high crop during the study interval.
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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 | 0.001 |
| 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".