River stream flows in the northern Québec Labrador region: A multivariate change point analysis via maximum likelihood
Bibliographic record
Abstract
Spring stream flows of six rivers that flow in the northern Québec Labrador region are modeled in a multivariate Gaussian framework and have been analyzed for possible change points in their average flows. The multivariate formulation takes into account correlations among the flows of the six rivers that flow in the same region. Significant change was detected in the multivariate mean vector, and the unknown change point is estimated by the method of maximum likelihood estimation. We then establish the asymptotic distribution of the change point maximum likelihood estimate, wherein we show that the multivariate case can be transformed into an equivalent univariate problem. A simulation study is carried out to investigate the robustness of the asymptotic distribution to departures from normality and independence as well as its closeness to finite samples. The asymptotic distribution allowed us to compute confidence interval estimates of the change point in the river flows. A decrease in the mean river flows in 1984 identified by the methodology may have been a consequence of a sharp decline in the region's snow cover that occurred about a year or two ahead.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".