Experimental Assessment of the Performance of High-Frequency CODAR and WERA Radars to Measure Ocean Currents in Partially Ice-Covered Waters
Bibliographic record
Abstract
Abstract High-frequency radars (HFRs) measure ocean surface currents remotely through the Bragg scattering of radio waves by surface gravity waves with wavelengths shorter than 50 m. HFR range is affected by sea ice, which dampens surface gravity waves and limits wind fetch for adjacent open waters. HFR range sensitivity to sea ice concentration was empirically determined for two types of HFR—Coastal Ocean Dynamics Applications Radar (CODAR) and Wellen Radar (WERA)—installed on the shores of the lower St. Lawrence estuary, Canada, during winter 2013. One CODAR was operating at 13.5 MHz on the southern shore, and one WERA was operating at 16.15 MHz on the northern shore. Ranges were determined using a signal-to-noise ratio threshold of 6 dB for first-order Bragg scattering measured by the receive antenna elements. Ranges were normalized for expected ranges in ice-free conditions, using empirical relationships determined during summer 2013 between the range and surface gravity wave energy at the Bragg frequencies. Normalized ranges Γ decrease approximately linearly with increasing sea ice concentration C (averaged over the ice-free observational domain) with a slope close to −1 for both HFR types, that is, Γ = 1 − C. However, for a given sea ice concentration, range can vary significantly depending on the sea ice spatial distribution.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".