Spatial analysis of black spruce (<i>Picea mariana</i> (Mill.) B.S.P.) radial growth response to climate in northern Québec – Labrador Peninsula, Canada
Bibliographic record
Abstract
The aim of this study is to analyze the relationships between black spruce (Picea mariana (Mill.) B.S.P.) growth and climate at a large spatial scale in North America’s northeastern boreal forest. The study area (approximately 700 000 km2) is located in the taiga zone of the Quebec – Labrador Peninsula. A network of tree-ring chronologies from 93 black spruce populations was developed. A hierarchical cluster analysis was conducted to analyze tree-ring series affinities, and response functions were calculated to analyze relationships between tree rings and climate. The cluster analysis results showed well-marked spatial affinities among the tree-ring series. These affinities were strongly linked with the spatial variability of the relationships between tree rings and climate. The interannual growth variations were governed mainly by the temperature variables that preceded the growing season (November (negative influence), December–January (positive influence), and April (positive influence)). The growing-season temperature (July temperature) mainly influenced the northernmost populations. Relationships between tree rings and climate in the northeastern boreal forest varied at a large spatial scale. This variability was expressed by a north–south contrast, which appears to be related to a temperature gradient, and an east–west contrast linked to a humidity gradient, which favors winter snow cover.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".