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Record W2162917604 · doi:10.1007/s00300-011-1010-5

Cetacean surveys in the Southern Ocean using icebreaker-supported helicopters

2011· article· en· W2162917604 on OpenAlexaff
Meike Scheidat, Ari S. Friedlaender, Karl‐Hermann Kock, Linn Sophia Lehnert, Olaf Boebel, Jason J. Roberts, Rob Williams

Bibliographic record

VenuePolar Biology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFisheryMarine protected areaOceanographyWhaleBiologyEcologyHabitatGeology

Abstract

fetched live from OpenAlex

Cetaceans in the Southern Ocean are potentially impacted by anthropogenic activities, such as direct hunting or through indirect effects of a reduced sea ice due to climate change. Knowledge on the distribution of cetacean species in this area is important for conservation, but the remoteness of the study area and the presence of sea ice make it difficult to conduct shipboard surveys to obtain this information. In this study, aerial surveys were conducted from ship-based helicopters. In the 2006/07 (ANT XXIII/8) and 2008/09 (ANT XXV/2) polar summers, the icebreaker RV ‘Polarstern’ conducted research cruises in the Weddell Sea, which offered the opportunity to use the helicopters to conduct dedicated cetacean surveys. Combining the results from both cruises, over 26,000 km were covered on survey effort, 13 different cetacean species were identified, and a total of 221 cetacean sightings consisting of a total of 650 animals were made. Using digital photography, it was possible to identify four different beaked whale species and to conduct individual photo-identification of humpback and southern right whales. Helicopter surveys allow the collection of additional information on sightings, (e.g. group size, species), as well as the coverage of areas with high ice coverage. The flexibility and manoeuvrability of helicopters make them a powerful scientific tool to investigate cetaceans in the Southern Ocean, especially in combination with an icebreaker.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.001

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 head0.059
GPT teacher head0.261
Teacher spread0.202 · 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

Citations32
Published2011
Admission routes1
Has abstractyes

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