Common Loon, <em>Gavia immer</em>, Breeding Success in Relation to Lake pH and Lake Size Over 25 Years
Bibliographic record
Abstract
I monitored Common Loon (Gavia immer) breeding success in relation to lake pH (range 4.0–8.5) between 1982 and 2007 on 38 single-pair lakes (5–88 ha) in the Sudbury, Ontario, area. No chicks fledged on lakes with pH < 4.4. Chicks fledged on lakes with slightly higher pH only if the lakes were relatively large. Acidic lakes became less acidic as sulphur dioxide emissions from the Sudbury smelters and sulphur deposition from other long-range sources decreased. Two lakes initially too acidic to support successful loon reproduction eventually had successful reproduction. One loon pair used two large acidic lakes (combined area 140 ha) connected by shallow rapids, and one of the adults made extremely long dives (average = 99 s) while foraging for the chicks. One chick died on that lake after apparently ingesting a very large food item; the lack of smaller items was attributed to the lake’s acidity. My results suggest that a shortage of food for chicks is the main reason why low pH reduces breeding success. I suggest that, for lakes without high levels of dissolved organic carbon (DOC), the critical pH for loon breeding success is approximately 4.3, and the suboptimal pH is approximately 4.4–6.0.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".