Evaluation Salish Sea marine bird Indicators with insights from recent research by professional and citizen scientists
Bibliographic record
Abstract
Marine birds are often viewed as good ecological indicators because they are relatively well studied and time-series data are often available, our understanding of their population biology is often extremely high, some species are tightly linked to their prey resources and, as upper trophic predators, they offer an integrative view of the dynamics at lower levels of the food web. In 2014, at-sea abundance and trends of the rhinoceros auklet, pigeon guillemot, marbled murrelet and scoters were collectively selected by the Puget Sound Partnership as indicators of the health of the Puget Sound marine food web. Long-term trends for these species are mixed with some species exhibiting relatively stable populations (e.g., rhinoceros auklet) and others are decreasing (e.g., marbled murrelet). In the absence of additional information, it is difficult to identify population change drivers. Fortunately, ongoing research by U.S. and Canadian academic and governmental researchers and citizen scientists (e.g., COASST, Puget Sound Seabird Survey, and Guillemot Research Group) are providing new insights into both population distributions and changes in population abundance. Specifically, these efforts have: (1) identified hotspots of species distributions, (2) evaluated the role of contamination, plastics and disease on population health, (3) evaluated the relative influence of various marine factors on population distribution and abundance, and (4) provided critical measurements of bird vital rates, measurements that are key to understanding population changes.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".