Complete dataset of loon productivity for Fuirst et al.
Bibliographic record
Abstract
The Common Loon (<i>Gavia immer</i>) is an iconic waterbird of the northern wilderness and an indicator of the health of aquatic ecosystems. We conducted a benchmark comparison of productivity rates and long-term trends in productivity (six-week-old young raised per territorial pair) from 46,401 breeding events across 12 different study populations throughout the southern portion of species’ breeding range in North America. While all regions showed long-term mean productivity rates at or above the threshold considered necessary for population stability (0.48 chicks fledged per territorial pair), we found significant declines in productivity (-0.17− -2.8% per year over 19−48 years) in seven regions, non-significant but downward trends in four others, and a non-significant increase in one region (Maine). Even though productivity is only one of three demographic parameters that dictate the stability of a population, long-term decreases in productivity signal looming population declines in Ontario, the Prairie and Atlantic provinces, New York, Massachusetts, and New Hampshire. We suggest that a variety of region-specific anthropomorphic and natural factors, in addition to increased rainfall secondary to climate change are at fault for such declines. Discovery of causes of productivity decline might lead to successful efforts to mitigate them, thus stabilizing or increasing loon populations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.048 | 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 teacher head, 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".