Multiscale analysis of typhoon-induced oceanic responses: A Case Study of Typhoon Kalmaegi in the South China Sea
Bibliographic record
Abstract
A localized multiscale energetics framework is used to study the multiscale typhoon-induced upper oceanic responses, in the case of Typhoon Kalmaegi in the South China Sea. A diagnostic methodology of the time-varying energetics, on the basis of the multiscale window transform (MWT) —namely, localized multiscale energy and vorticity analysis (MS-EVA) decomposes HYCOM variable fields into a low-frequency background flow window, a mid-frequency flow window and a high-frequency process window. The background window represents mesoscale processes and Kuroshio currents well and the mid-frequency window captures near-inertial processes influenced by typhoon-induced wind stresses. The scale-scale kinetic energy transfers from the near-inertial window to the background window, mainly on the right-hand side of the typhoon track. Advection and pressure work redistribute energy contribute to the accumulation of kinetic energy in the mid-frequency flow window and enhances ocean mixing. Negative vorticity has a significant impact on the distribution and downward propagation of the near-inertial energy, leading to heterogeneity in the mixing of the upper ocean. We offer new insights into understanding the multiscale interactions between typhoons and the upper ocean.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".