Artificial-intelligence-based detection tests for the identification of shifts and trends in Canadian hydrometric data
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Bibliographic record
Abstract
Two artificial-intelligence-based detection tests for the identification of shifts and trends in data sequences are described and applied to Canadian hydrometric data. These tests are based on the Kohonen neural network and fuzzy c-means approach. They are applied for the detection of shifts and trends in annual mean and daily maximum streamflow data from 43 Canadian hydrometric stations. The results of the tests are compared with those from conventional detection tests, such as the Mann–Whitney test for shifts and the Mann–Kendall test for trends. These results support conclusions from previous studies about the presence of trends in Canadian hydrometric data. As a whole, the artificial-intelligence-based and conventional tests may be used to confirm one another. In some cases, given their respective strengths and weaknesses, the tests may complement one another. One should therefore consider the use of more than one detection test for determining the presence or absence of anomalies in data sequences.Key words: hydrometric data, detection tests, shifts, trends, artificial intelligence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it