Analyse des changements de régimes dans les séries temporelles issues de la dendrochronologie
Bibliographic record
Abstract
The problem of switching regimes in hydrological time series led to serious questioning in Québec during the last decades. In fact, when we examine annual inflow and precipitation time series of a number of Québec watersheds, we can identify alternating sequences of high and low values. These variations must be taken into account in the hydrological forecasting process. However, since we only have limited information (no more than five decades of observations), we may examine different natural archives such as trees to study hydroclimatic variability over a longer period. One of the objectives of the ARCHIVES project was therefore to study hydrometeorological regime changes in time series reconstructed using dendrochronology. In this chapter, we address the problem of changepoint analysis in such time series by using finite mixtures of distributions. Mixtures of distributions become natural models to represent datasets in which observations may originate from several distinct statistical populations. The problem is treated from a Bayesian perspective. Our approach was applied to several time series reconstructed in the ARCHIVES project. These applications allowed us to identify regime changes that are spatially consistent, as well as historical changepoints that correspond to specific events such as abrupt changes in summer temperatures that coincide with strong volcanic eruptions. In this chapter, we describe the approach and illustrate it by analysing time series of summer temperatures and water inflows reconstructed using black spruce (Picea mariana Mill. BSP) tree-rings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".