Predicting tidal heights for new locations using 25 hours in situ sea-level observations plus reference site records: a complete tidal species modulation with tidal constant.
Bibliographic record
Abstract
A hybrid technique for predicting tides for new locations, based on as little as 25 h of concurrent temporary \nand reference site sea level observations, plus up to a year of reference records, is evaluated using 2-yr South \nKorean and New Zealand case studies. Comparisons are made between the existing prediction methods of \nconventional standard harmonic analysis and prediction (CSHAP) and tidal species modulation with tidal \nconstant corrections (TSM1TCC). Building on these approaches, a new procedure is developed to produce \na complete tidal species modulation (CTSM) equivalent of CSHAP, with the added inclusion of nodal factors \nand angles, astronomical arguments, and tidal species tidal constant correction terms (1TCC), to generate \nresults for temporary sites. The CTSM1TCC approach described here overcomes the record length limitations \nof traditional standard harmonic-based prediction methods, making the technique more useful to diverse \ncoastal and hydrographic researchers. \nThe CTSM1TCC method is refined using yearlong input and comparative data from contrasting hydrographic \nsettings, revealing spring periods, specific months, and conditions devoid of nontidal residual extremes \n(e.g., storms) as the most appropriate sample periods for collecting temporary site data in order to \nmaximize prediction accuracy. CTSM1TCC represents a viable alternative to tidal prediction methods using \nmulticonstituent inferences, for those wishing to make predictions for new sites based on established conventional \ntidal prediction software, with the added benefits of efficient input data collection and no need for \na decision process regarding multiconstituent inference calculations. CTSM1TCC could, without compromising \naccuracy, support the spatial and temporal proliferation of tidal predictions across coastal oceans, \nwhere fieldwork funds and instruments currently hinder predictions for new locations.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".