Bibliographic record
Abstract
Altered environmental conditions such as changes in the distribution and phenology of plants and animals have been linked to local or regional climate change; polar and alpine species are particularly vulnerable (Parmesan 2006). One habitat that is showing rapid change is the sea ice of the circumpolar Arctic. The Arctic and subarctic regions are undergoing large changes in response to global warming due to feedback processes referred to as polar amplification (e.g., IPCC 2007). These regions are characterized by an acceleration of climatic and environmental change. Loss of sea ice is dramatic and the rate of change unprecedented (e.g., Stroeve et al. 2014). The western Hudson Bay sea ice extent (defined as the area of the ocean with a fractional ice cover/concentration ≥15%) has declined significantly since 1979 (Stern & Laidre 2016). Polar bears (Ursus maritimus) that inhabit this region are vulnerable to climate warming (Stirling & Derocher 2012; Lunn et al. 2016). Polar bears depend on sea ice as the platform from which they hunt, travel, and mate (Smith 1980; Johnson et al. 2019). They are an obligate predator with ringed seals (Pusa hispida) and bearded seals (Erignathus barbatus) the primary prey and the survival and reproduction of polar bears is dependent on the acquisition of sufficient adipose reserves obtained in spring and early summer (Stirling & Archibald 1977; Ramsay & Stirling 1988). The nearshore ice has been identified as an important habitat for polar bears in many areas (Stirling, Andriashek & Calvert 1993; McCall et al. 2016). Further, polynyas and flaw lead systems (areas of consistently open water in winter) have been identified as important habitats for Arctic marine systems (Stirling 1980; Stirling 1997). Climate change induced loss of sea ice is affecting the 19 polar bear populations that range across the circumpolar north to varying degrees (Regehr et al. 2010; Derocher et al. 2013). The Western Hudson Bay population, which summers along the coast of Manitoba and southern Nunavut, has been identified as being at-risk from an increasing ice-free period (Stirling, Lunn & Iacozza 1999; Lunn et al. 2016). The recent trend of later sea ice freeze-up and earlier sea ice break-up has forced the bears to spend longer onshore away from their prey (Regehr et al. 2007; Castro de la Guardia et al. 2013). Over 1979-2014, the sea ice cover duration in the area used by Western Hudson Bay polar bear population declined by 8.6 days/decade (Stern & Laidre 2016). Changes in sea ice condition have been associated with declines in cub survival, atypical hunting, cannibalism, drowning, starvation, and more problem bears (Stirling & Parkinson 2006; Towns et al. 2009; Stirling & Derocher 2012; Heemskerk et al. 2020). How bears cope with reduced sea ice habitat provides insight to how they may respond to ongoing climatic change. In this study, I propose to examine polar bear distribution, habitat use, and movement patterns using ear tag satellite-linked telemetry during the spring feeding period in the nearshore areas of western Hudson Bay. These tags provide a new means of obtaining insights on parts of the population that could not be followed with conventional collaring methods. The same ear tag radios were deployed in collaboration with the Polar Bear Alert Program in autumn.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".