Modelling of architectural development of black spruce (Picea mariana Britton., Stern & Poggenb.) regarding climate
Bibliographic record
Abstract
Climate change is a major concern on forest: how will the current stand response to this change? Plant architecture is a science that allows studying the morphology of plants. Under contrasted climate (such as temperate climate), plant growth is not continuous: there are several breaks (particularly in winter). For several species these breaks remain visible for many years (leaving marks like scars). Black spruce (Picea mariana Britton., Stern & Poggenb. 1988) follows this pattern. We then studied architecture from mixed stand (Parc des Grands jardins, Québec) in order to assess the climate response of Black spruce architecture. We studied these parameters:\n-annual shoot length\n-ramification\n-needle length\n-reproduction\nFor each parameter, we computed a PLS (Partial least square) regression (linear or logistic) with climatic parameters (monthly rainfall and temperature) and topologic variables (such as ramification order, age ...). The results of this analysis allow finding the active periods (organogenesis and elongation). We aggregated this result to build a simulator of Black spruce development depending on a climatic scenario.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".