Evaluation of a basic procedure to assess climate impact on sea ice: Hudson Bay as a test case
Bibliographic record
Abstract
In Northern Canada, Inuit communities rely heavily on sea ice for commuting, hunting, fishing and other traditional activities. From their perspective, this platform may be considered an infrastructure in its own right. Over the last several decades, the extent, thickness, surface conditions and overall dynamics of sea ice have been affected by climate change. While scientists are actively studying these phenomena, the observers best able to contribute answers to key questions are the people that actually use the ice and experience that environment on a day-to-day basis, i.e. the Inuit. What is presented in this report is a method to research climate change impact, which could be conducted by members of the Northern communities. This is done through the combined use for two software platforms. The first one is the Canadian Arctic Shipping Risk Assessment System (CASRAS), a user-friendly database developed by NRC, which incorporates ice maps from the Canadian Ice Service (CIS) as well as historical datasets on climate. With Microsoft (MS) Excel, the second software, parameters such as air temperature and wind data were extracted from CASRAS and analyzed. For this exercise, the report focuses specifically on ice cover formation and break-up in Hudson Bay, over the last 30 to 40 years. Statistically significant trends were identified. The ice cover in that inland sea has required more time to develop into a fully established ice cover (an increase of 3 to 4 days per decade). Ice break-up initiation has begun earlier in the Spring/Summer, i.e. that shift is estimated at about 5 days per decade. In Arviat, an increase of about 0.3 m/s per decade in wind speed is documented in the Fall. Because these observations are for the whole of Hudson Bay, they provide a general perspective on the overall evolution of the ice cover (as opposed to local conditions). Ultimately, this type of work could be fed into climate models of future ice conditions.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".