A Sea-Surface Temperature Homogenization Blend for the Northwest Atlantic
Bibliographic record
Abstract
As part of the Atlantic Zone Monitoring Program two different methods were applied to merge three Level-3 AVHRR SST products into a homogenized blend covering the time period from 1982 to present over the Northwest Atlantic, providing a regionally-tuned climatological base on which to assess whether current observations are below, near or above normal. Weekly and monthly SST composites were constructed by averaging daily anomalies within each time period and adding the result to the climatological mean for the period. This approach reduces biases introduced from missing data during a strong warming/cooling seasonal period. Since AVHRR SST data have many spatial and temporal gaps, a common difficulty is establishing how much data are sufficient to yield useful estimations of temperature anomalies. A statistical Monte Carlo method showed that monthly and weekly regional averages composed respectively of as little as 7% and 10% of possible data still yield useful results. A test case shows the increased usefulness of the blend for State of the Ocean reporting. Application of the data set confirmed the use of coastal air temperature as a useful proxy for SST allowing hindcasting past changes or forecasting future changes associated with global warming over Eastern Canadian waters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".