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ESTIMATING PUP PRODUCTION OF HARP SEALS, <i>PAGOPHILUS GROENLANDICUS</i>, IN THE NORTHWEST ATLANTIC

2003· article· en· W2086511952 on OpenAlexaffabout
Garry B. Stenson, Louis‐Paul Rivest, Michael O. Hammill, Jean‐François Gosselin

Bibliographic record

VenueMarine Mammal Science · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversité LavalFisheries and Oceans Canada
Fundersnot available
KeywordsAerial surveyOceanographyHARPGeographyLittle ice ageFisheryEnvironmental scienceHerdAnimal sciencePhysical geographyBiologyGeologyCartographyClimate change

Abstract

fetched live from OpenAlex

A bstract Photographic and visual aerial surveys to determine current pup production of Northwest Atlantic harp seals were conducted off Newfoundland and in the Gulf of St. Lawrence during March 1999‐Photographic surveys were conducted on all whelping concentrations between 14 and 24 March, whereas a visual survey was made of the southern Gulf concentrations on 14 March. Pup production was estimated to be 739,100 (SE = 96,300, CV = 13.0%) at the Front, 82,600 (SE = 22,500, CV = 27.2%) in the northern Gulf, and 176,200 (SE = 25,400, CV = 14.4%) in the southern Gulf (Magdalen Island) for a total of 997,900 (SE = 102,100, 10.2%). Changes in aerial survey estimates indicate that pup production has increased since 1994. A new method to correct for the temporal change in the proportion of pups present on the ice was examined by fitting the percentage of pups observed in three age‐dependent stages to a Normal distribution. The results were compared to those obtained from a more complex model used previously. The Simple model produced slightly higher, and hence more conservative, estimates of the proportion of births that had occurred before the time of the survey than the Complex model. When using the Simple model fewer assumptions regarding the start date of pupping and the proportion of older pups remaining on the ice were required, the herd had to be followed for a shorter period, and a more convenient means of calculating confidence limits was available.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.045
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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.

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

Citations37
Published2003
Admission routes2
Has abstractyes

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