Bibliographic record
Abstract
observations and assimilation methodologies are refined. Assimilation schemes for shelf waters are complicated by the presence of strong fronts coincident with abrupt topographic variations and degraded altimetry in the near-shore region (Mourre et al. 004). It is clear that both higher-density obser-vations and more sophisticated assimilation schemes will be required for the shelf regions. Again AZMP will be providing valuable data for fine tuning assimilation schemes for the shelf that are under development. Conclusion Clearly an expansion in Canadian operational oceanographic capacity will require increased input from AZMP and analogous programs. These data are essential to the development of an operational capacity and will become even more valuable as this system matures. There will be demands to have access to the data from AZMP surveys in near real time in order to ensure their use in short-term ocean forecasts for the regional and basin-scale forecasting systems. This will require further devel-opment from both the data acquisition and the modelling initia-tives and will be possible only through intensive collaboration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.435 | 0.322 |
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".