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Record W1517977935 · doi:10.22621/cfn.v123i2.693

Common Loon, <em>Gavia immer</em>, Breeding Success in Relation to Lake pH and Lake Size Over 25 Years

2009· article· en· W1517977935 on OpenAlexfundvenueaboutno aff
Robert Alvo

Bibliographic record

VenueThe Canadian Field-Naturalist · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersCanadian Wildlife FederationMinistry of Natural ResourcesWorld Wildlife Fund
KeywordsForagingRange (aeronautics)Economic shortageEcologyReproductionDeposition (geology)Environmental scienceFisheryBiologySediment

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.008
GPT teacher head0.217
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2009
Admission routes3
Has abstractyes

Explore more

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