How Arctic Marine Birds Help Researchers Study a Changing North
Bibliographic record
Abstract
T he Arctic region is currently undergoing environmental change at an unprecedented rate (IPCC, 2007).Changing climatic conditions, a growing tourism industry, increasing levels of development and the associated marine shipping, and a growing human population are a few of the accumulating challenges the circumpolar Arctic is now facing (ACIA, 2004).The Canadian Arctic is no exception.Recent warming trends in Arctic Canada have led to reduced summer sea ice extent, as well as changes in snow line elevation and snowmelt (Wang and Overland, 2012;Miller et al., 2013).With the development of the tourism industry and the natural resource sector in northern Canada, ship traffic is predicted to increase, particularly around Baffin Island and the Northwest Passage (Smith and Stephenson, 2013;Dawson et al., 2014).In a time of rapid change, studies that examine how changes are affecting both the people and the environment are needed to develop evidence-based management and adaptation strategies (Armitage et al., 2011;Bring and Destouni, 2014).With more than 36 000 islands and 162 000 km of coastline, the marine environment represents a large component of the Canadian Arctic.Although the magnitude of the northern marine environment makes it challenging to conduct research there, access can be enhanced through the involvement and participation of the many communities that are widely distributed throughout the region.An additional approach when working in this geographically large and diverse region is to examine "indicator species": those that, in addition to being the focus of specific questions, also support a wider array of research objectives.As one example, marine birds are recognized as important global sentinels in marine ecology (Piatt et al., 2007), as well as in northern environments (Karnovsky et al., 2008).By definition, marine birds spend most of the year at sea, typically dispersed across vast tracts of ocean, but each summer they must return to land to breed, often in large nesting colonies (Gaston, 2004).This annual pattern allows research programs to establish protocols that are repeatable from year to year and to support research questions that benefit from long-term data sets (Gaston et al., 2009).Given the number of birds nesting at colonies, seabirds also offer larger sample sizes than many other wildlife species studied: researchers can often monitor tens of thousands of individuals at one location (Piatt et al., 2007).It is important that marine birds are also among the few marine indicator species that regularly visit the terrestrial environment, Jennifer Provencher is the 2014 recipient of the Jennifer
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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 | 0.031 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".