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Record W4240917042 · doi:10.22621/cfn.v129i2.1695

Seventeenth census of seabird populations in the sanctuaries of the North Shore of the Gulf of St. Lawrence, 2010

2015· article· en· W4240917042 on OpenAlexfundvenueaboutno aff
Jean‐François Rail, Richard Cotter

Bibliographic record

VenueThe Canadian Field-Naturalist · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersParks Canada
KeywordsSeabirdUria aalgeGeographyShoreCensusPopulationFisheryEcologyBiologyDemographyPredation

Abstract

fetched live from OpenAlex

Seabirds in the 10 migratory bird sanctuaries of the North Shore of the Gulf of St. Lawrence, Quebec, Canada, which were created in 1925, have been censused regularly for the last 85 years. The sanctuaries support 16 seabird species, many of which are found in significant numbers. From 2005 to 2010, some notable population changes were observed: large increases in Common Murres (Uria aalge), Razorbills (Alca torda), and two species of cormorants and continuing declines in Black-legged Kittiwakes (Rissa tridactyla) and Atlantic Puffins (Fratercula arctica). The status of Leach’s Storm-Petrel (Oceanodroma leucorhoa) and Caspian Tern (Hydroprogne caspia) is extremely precarious because of their small breeding populations. Between 2005 and 2010, seabird numbers in the sanctuaries increased 19% overall and were stable in most sanctuaries (≤ 15% change); however, notable increases were observed at Îles Sainte-Marie (60%), Baie des Loups (47%), and Île à la Brume (44%). Nonetheless, considering historical records, increased surveillance and raising of awareness of seabird conservation in local communities near the sanctuaries of Île à la Brume, Baie des Loups, and Saint-Augustin would be most beneficial.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.037
GPT teacher head0.252
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2015
Admission routes3
Has abstractyes

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