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Record W2338114345 · doi:10.15273/pnsis.v47i1.3378

BEACHED BIRD SURVEYS ON SABLE ISLAND, NOVA SCOTIA, 1993 TO 2009, SHOW A DECLINE IN THE INCIDENCE OF OILING

2012· article· en· W2338114345 on OpenAlexaffvenueabout
Zoe Lucas, Andrew G. Horn, Bill Freedman

Bibliographic record

VenueProceedings of the Nova Scotian Institute of Science · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsDalhousie University
FundersU.S. Department of Energy
KeywordsWaterfowlNova scotiaSeabirdGeographyFisheryShoreEndangered speciesOceanographyEcologyPredationBiologyArchaeologyHabitatGeology

Abstract

fetched live from OpenAlex

Sable Island, located about 160 km southeast of the landmass of NovaScotia, Canada, is far offshore and provides a platform for beach surveysto monitor oil pollution in Scotian Shelf waters. Sporadic beach surveysconducted there during the 1970s and 1980s indicated that oiled birds andbeached tar were common occurrences. During a survey program fromJanuary 1993 to December 2009, more than 10,800 bird corpses werefound in 171 surveys covering a total of >13,500 km of shoreline. Sixtyfourspecies were recorded, of which 52 were seabirds and waterfowl.The numbers of beached birds and species composition exhibited largefluctuations, which reflected both the seasonal distribution of species andthe effects of weather and beach conditions. The oiling rate of corpsesfor all seabirds and waterfowl combined was 28.6%, and ranged from ahigh of 69.9% in 1996 to a low of 1.4% in 2009. Alcids had the highestrates of oiling (averaging 54.3%), while lower rates were observed forshearwaters (1.9%) and Larus gulls (2.4%). The results of the 1993-2009surveys, as well as those of earlier studies in the 1970s and 1980s, indicatea declining trend in the oiling rate of beached birds on Sable Island.Keywords: oil pollution, marine pollution, seabird oiling rate, beachedbird survey, Sable Island

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.289
Teacher spread0.250 · 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

Citations9
Published2012
Admission routes3
Has abstractyes

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