Spatial clustering of seasonal sea ice of Hudson Bay, Canada, 1971–2018
Bibliographic record
Abstract
The spatial variation in seasonal sea ice in Hudson Bay is examined using spatial clustering analyses. For the period 1971 to 2018, a time series of sea-ice breakup and freeze-up dates and ice-free season length at thirty-six grid locations is generated from sea-ice charts derived from satellites and other data. These data are analyzed spatially using three different clustering techniques. Overall, the three methods revealed a northeast/southwest axis in sea-ice behavior consistent with a well-documented cyclonic current flow and wind regime in Hudson Bay. The methods did differ in assigning grid locations to clusters with the greatest consistency for breakup behavior and in the northern region of the Bay across all three metrics. The greatest variability occurred with the central sea-ice platform likely the response to only subtle variations among these locations, leading to varying clusters. Missing data especially with freeze-up and ice-free season played a role in the clustering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".