Climate and environmental drivers of berry productivity from the forest–tundra ecotone to the high Arctic in Canada
Bibliographic record
Abstract
Berry shrubs are found across the circumpolar North where they are an important source of food for people and animals. However, the environmental controls on berry productivity in these regions is poorly understood. This study presents the results of an ongoing berry productivity monitoring program for Empetrum nigrum L., Vaccinium uliginosum L., and Vaccinium vitis-idaea L. from the forest–tundra ecotone to the high Arctic in Canada. Berry productivity was the highest recorded for these species with up to 119 berries/m2 (E. nigrum) and 661 berries/m2 (V. uliginosum) measured at one plot in Pangnirtung. On average, berry productivity for E. nigrum and V. uliginosum was higher toward the northern edge of the species distribution range. The climate variables important for the productivity of V. uliginosum in high Arctic sites were closely associated with the onset of the growing season and water availability during the growing season, whereas those important in the low Arctic sites reflected conditions during the growing season. None of the climate variables used were associated with the productivity of E. nigrum and V. vitis-idaea, likely due to complex responses and length of the time-series, thus highlighting the importance of continued monitoring in partnership with northern people and institutions.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".