Time series data for Canadian arctic vertebrates: IPY contributions to science, management, and policy
Bibliographic record
Abstract
Abstract Long-term data are critically important to science, management, and policy formation. Here we describe a number of data collections from arctic Canada that monitor vertebrate population trends of freshwater and marine fish, marine birds, marine and terrestrial mammals. These time series data cover the last ca. 30 years and capture a period from the onset of global changes affecting the Arctic up to recent years with a rapid increase in temperature. While many of these data collections were initiated through a variety of government and university programs, they also include a surge in polar research launched with the recent International Polar Year (2007–2008). We estimated the long-term vertebrate index from our data that summarizes various taxa abundance trends within a global context and observed a continuous decline of about 30 % in population abundance since the 1990s. Though most data collections are biased towards few taxa, we conduct time-series analyses to show that the potential value of long-term data emerges as individual monitoring sites can be spread across space and time scales. Despite covering a handful of populations, the different time series data covered a large spectrum of dynamics, cyclic to non-cyclic, including coherence with the North Atlantic Oscillation, lag effects, and density dependence. We describe a synthesis framework to integrate ecological time-series research and thereby derive additional benefits to management, science, and policy. Future requirements include: (1) continuation of current observation systems; (2) expansion of current monitoring sites to include additional trophic links and taxonomic indicators; (3) expansion beyond the existing program to include greater spatial coverage into less-sampled ecosystems and key representative locations; and (4) integration of circumpolar observations and comprehensive analyses. Development of a circumpolar observation system is necessary for innovative science, large-scale adaptive management, and policy revision essential to respond to rapid global change.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".