Reconstitution des apports en eau des deux derniers siècles : approche méthodologique. Application au réservoir Caniapiscau (Complexe La Grande, Québec)
Bibliographic record
Abstract
The objective of this study was to reconstruct 200 years of spring (Qspr), summer (Qsum) and annual (Qann) water supply variability at the Caniapiscau Reservoir in the remote area of northern Quebec. This key area for hydropower production lacks long-term hydrological records, and tree-ring proxies are thought to be the best substitute for extending the instrumental climatic records. Tree-ring widths, ring densities and stable isotope ratios (delta-13C and delta-18O) were used to perform paleohydrological reconstructions. The following reconstruction techniques were evaluated for each variable reconstructed: partial least square (PLS) regression applied to all of the tree-ring series, PLS regression applied to selected tree-ring series, and the best analogue method (BAM) applied to selected tree-ring series. These three reconstructions were then combined into a composite reconstruction. Reconstruction verification shows that the annual and summer water supply reconstruction quality is good, while the verification tests disqualified our spring water supply reconstruction. The reconstructed long-term water supply variations over the past two centuries are dominated by decadal to sub-decadal fluctuations, and also show that there are distinct long hydrological periods during which water supplies change in intensity and variability. Annual water supplies (Qann) inversely correlate with both winter and summer North Atlantic Oscillation (NAO) indices. Arctic oscillation (AO) indices also influence Qann to a greater degree than NAO indices. Summer water supplies inversely correlate only with AO and NAO summer indices.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".