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Record W2779141614 · doi:10.1007/s10236-017-1122-8

A note on interpreting tidal harmonic constants

2017· article· en· W2779141614 on OpenAlex
Zhigang Xu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOcean Dynamics · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsInterpolation (computer graphics)AmplitudeNonlinear systemHarmonicEllipseMathematical analysisMathematicsPhase (matter)Constant (computer programming)Applied mathematicsPhysicsGeometryComputer scienceAcousticsClassical mechanicsOptics

Abstract

fetched live from OpenAlex

The amplitudes and phase lags of tides or tidal currents, which are collectively known as tidal harmonic constants (THCs), cannot be interpolated linearly and separately. Mistreatment for the interpolations could occur, and indeed, it is seen even in the literature. This note clarifies this topic by providing correct formulas to interpret THCs. One has to perform a nonlinear and coupled interpolation of THCs to have equivalence to the liner interpolation in the time domain. Similarly, tidal current ellipse parameters (ep-parameters) cannot be interpolated linearly and separately. To interpolate ep-parameters, this note recommends that one should first convert them to amplitude and phase lag parameters (ap-parameters) and then use the nonlinear and coupled interpolants proposed herein to interpolate the ap-parameters, followed by converting the results back to ep-parameters. Examples are provided to illustrate the problems and MATLAB functions are provided in Appendix as interpolating tools.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.230
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it