Introduction to Special Collection on Arctic Ocean Modeling and Observational Synthesis (FAMOS) 2: Beaufort Gyre Phenomenon
Bibliographic record
Abstract
Abstract One of the foci of the Forum for Artic Modeling and Observational Synthesis (FAMOS) project is improving Arctic regional ice‐ocean models and understanding of physical processes regulating variability of Arctic environmental conditions based on synthesis of observations and model results. The Beaufort Gyre, centered in the Canada Basin of the Arctic Ocean, is an ideal phenomenon and natural laboratory for application of FAMOS modeling capabilities to resolve numerous scientific questions related to the origin and variability of this climatologic freshwater reservoir and flywheel of the Arctic Ocean. The unprecedented volume of data collected in this region is nearly optimal to describe the state and changes in the Beaufort Gyre environmental system at synoptic, seasonal, and interannual time scales. The in situ and remote sensing data characterizing ocean hydrography, sea surface heights, ice drift, concentration and thickness, ocean circulation, and biogeochemistry have been used for model calibration and validation or assimilated for historic reconstructions and establishing initial conditions for numerical predictions. This special collection of studies contributes time series of the Beaufort Gyre data; new methodologies in observing, modeling, and analysis; interpretation of measurements and model output; and discussions and findings that shed light on the mechanisms regulating Beaufort Gyre dynamics as it transitions to a new state under different climate forcing.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.019 |
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".