Dévelopement d'un modèle hiérarchique Bayésien appliqué aux épaisseurs de cernes
Bibliographic record
Abstract
A basic principle of the dendroclimatology is that the annual tree-rings hide information on past climate. From a statistical viewpoint, all tree rings formed the same year belong to a given perimeter associated with a latent random variable - i.e. an unobservable random variable - integrating all forcings having affected trees of the perimeter during the period they formed their respective ring. This annual latent variable is therefore common of all trees of the investigated area. The succession of these latent variables constitutes temporal series characterizing the handling area and the work of the statistician is to mobilize all the available information to extract a chronicle that may receive a climatic interpretation. The clarity of the output signal varies depending on the tree species, regional factors and the statistical methods used. In the case of black spruce (Picea mariana Mill. BSP), very common in Northern Quebec, we developed a hierarchical Bayesian model named DENDRO-AR which essentially provides a chronological of posterior distributions of the latent variables, each synthesizing the influence of the environment on the growth of trees, including the climatic conditions that prevailed during the period of vegetation. Applying this model to a set of adequately distributed sites over a wide area, the mapping of a particular quantile, e.g. the median, authorizes a spatiotemporal analysis of the common signal.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".