Near-bottom temperature data obtained from Current and Pressure-recording Inverted Echo Sounders (CPIES) and processed HYCOM data near the Chukchi slope
Bibliographic record
Abstract
Quasi-seasonal and sub-inertial variations of near-bottom temperature were observed using Current and Pressure-recording Inverted Echo Sounders (CPIES) deployed on the slope between Canada basin and the Chukchi shelf from August 2018 to July 2020.Quasi-seasonal and sub-inertial variations of near-bottom temperature were observed using Current and Pressure-recording Inverted Echo Sounders (CPIES) deployed on the slope between Canada basin and the Chukchi shelf from August 2018 to July 2020. They were equipped with a 4930R ZPulse Doppler current sensor positioned approximately 51 m above the seafloor, which measured near-bottom current and temperature. Among the variables CPIES measured, we used the near-bottom temperature, the resolution and accuracy of which are 0.01°C and 0.1°C, respectively. We also used temperature, velocity, and sea surface height data from the Hybrid Coordinate Ocean Model (HYCOM). We collected outputs from the HYCOM + NCODA Global 1/12° GOFS 3.1 41-layer analysis (GLBy0.08 Experiment 93.0). It has a horizontal resolution of 0.08° × 0.04° (zonal and meridional, respectively) and a temporal resolution of 3 h.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".