Artificial-intelligence-based detection tests for the identification of shifts and trends in Canadian hydrometric data
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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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 | 0.014 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".