Sea Ice and Current Response to the Wind: A Vector Regressional Analysis Approach
Bibliographic record
Abstract
The authors describe a two-dimensional (vector) regressional model for examining the anisotropic response of ice drift and ocean current velocity (“drift velocity”) to surface wind forcing. Illustration of the method is limited to sea ice response. The principal mathematical and physical properties of the model are outlined, together with estimates of the “response matrices” and the corresponding “response ellipses” (drift velocity response to a unity wind velocity forcing). For each direction, φ, of the wind vector the method describes a corresponding “wind factor” α(φ) (relative drift speed) and “turning angle” θ(φ) (the angle between the drift velocity and wind vector). The major ellipse axis corresponds to the direction of the “effective wind” (φ = φ max ) and the minor axis to the direction of the “noneffective” wind. The eigenvectors of the response matrix define wind directions that are the same as the wind-induced drift velocity directions. Depending on the water depth and offshore distance, six analytical cases are possible, ranging from rectilinear response ellipses near the coast to purely circular response ellipses in the open ocean. The model is used to examine ice drift along the western shelf of Sakhalin Island (Sea of Okhotsk). Responses derived from the vector regression (four parameter) method are less constrained and therefore more representative of wind-induced surface motions than those derived using the traditional complex transfer function (two parameter) approach.
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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 | 0.001 | 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 | 0.000 |
| 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".