Bibliographic record
Abstract
Estimate non-stationary harmonic regression models for water level data -- adaptation to Python and generalization of the NS_Tide MATLAB tool (Matte et al, 2013). Related publications: Adaptation of classical tidal harmonic analysis to nonstationary tides, with application to river tides. P. Matte, DA. Jay, ED. Zaron. 2023. Journal of Atmospheric and Oceanic Technology 30 (3), 569-589. Temporal and spatial variability of tidal-fluvial dynamics in the St. Lawrence fluvial estuary: An application of nonstationary tidal harmonic analysis, P. Matte, Y. Secretan, J. Morin. 2014. Journal of Geophysical Research: Oceans 119 (9), 5724-5744. Analytical and residual bootstrap methods for parameter uncertainty assessment in tidal analysis with temporally correlated noise. S. Innocenti, P. Matte, V. Fortin, N. Bernier. 2022. Journal of Atmospheric and Oceanic Technology 39 (10), 1457-1481. Impact of storm surge and power peaking on tidal-fluvial processes in the microtidal Neretva River estuary. N Krvavica, MM. Grzic, S. Innocenti, P. Matte. 2025. Estuarine, coastal and shelf science 318, 109227. Tidal, hydrological and meteorological contributions to high-water level events in the Saint Lawrence River Estuary: local responses to regional drivers. S. Innocenti, M. Fortier, P. Matte, R. Gosselin, O. Champoux - Ready to be submitted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.131 | 0.104 |
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".