Bibliographic record
Abstract
ABSTRACT. Hudson Bay experiences a complete cryogenic cycle each year. Sea ice begins to form in late October, and the Bay is usually ice-free in early August. This seasonally varying ice cover plays an important role in the regional climate. To identify secular trends in the cryogenic cycle, we examined variability in the timing of sea-ice formation and retreat during the period 1971– 2003. The dates of ice freeze-up and breakup at 36 locations across Hudson Bay were catalogued for each year from weekly ice charts provided by the Canadian Ice Service. We used the nonparametric Mann-Kendall test to determine the statistical significance of the trends and the Theil-Sen approach to estimate their magnitude. Our results indicate statistically significant trends toward earlier breakup in James Bay, along the southern shore of Hudson Bay, and in the western half of Hudson Bay, and toward later freeze-up in the northern and northeastern regions of Hudson Bay. These trends in the annual ice cycle of Hudson Bay coincide with both the regional temperature record and the projections from general circulation models. If this trend toward a longer ice-free season continues, Hudson Bay will soon face important environmental challenges. Key words: breakup, climate change, freeze-up, Hudson Bay, sea ice, temperature RÉSUMÉ. Chaque année, la Baie d’Hudson connaît un cycle cryogénique complet. La formation de la glace marine commence en fin d’octobre et la baie est habituellement exempte de glace en début d’août. La présence saisonnière du couvert de la glace de la Baie d’Hudson revêt une importance primordiale sur le climat régional. Dans cet article, on étudie la variabilité des dates de formation et de retrait de la glace marine de la Baie d’Hudson dans le but d’identifier des tendances séculaires durant la période
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.310 | 0.208 |
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".