Bibliographic record
Abstract
This paper qualitatively discusses the changes in the directional wave field as the result of an atmospheric cold front passage. The directional wave data were measured using a 3-m discus buoy in the north-eastern Gulf of Mexico using a Longuet-Higgins technique with 46 frequency bands (0.0325 to 0.485 Hz). Changes in the directional wave spectra were examined during a 4-day period that included a major wind shift under both quasi-steady and nonsteady wind fields. A procedure of combining the wave-energy into four directional quadrants, in lieu of individual mean directions, was examined as an analytical time-series technique for directional wave spectra. The lag time between a wind speed increase and a corresponding increase in wave-energy was shown to be a function of the existing sea state with shorter lag times for a higher sea condition. A numerical model analysis of VAN VLEDDER and HOLTHUIJSEN (1988) indicated that after a major wind shift the wave-energy would not only move to lower frequencies, but would also rotate to realign with the direction of the older wind field. The conclusions of VAN VLEDDER and HOLTHUIJSEN (1988) were supported by direct observations of pre- and post-frontal wave fields. Hourly, polar contoured, directional wave spectra plots show a narrow frequency band energy bridge between the two wave fields. The development of an energy bridge is shown to correspond to a change in the decay rate of the old wave field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.010 | 0.004 |
| Insufficient payload (model declined to judge) | 0.991 | 0.993 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".