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Record W3092771800 · doi:10.1111/mms.12745

Assessing the lethality of ship strikes on whales using simple biophysical models

2020· article· en· W3092771800 on OpenAlexaff
Dan Kelley, James P. Vlasic, Sean W. Brillant

Bibliographic record

VenueMarine Mammal Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsCanadian Wildlife FederationDalhousie University
Fundersnot available
KeywordsWhaleLethalityCollisionMarine engineeringSimple (philosophy)MechanicsComputer scienceFisheryBiologyPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract Studies of ship strikes on whales often focus on large vessels (>20 m), with attention to their speeds and the resulting risk of lethality. Smaller coastal vessels also co‐occur with whales, resulting in collisions that merit study. To cast light on injuries caused by vessels of all sizes, we used knowledge of right whale anatomy and Newtonian mechanics to construct simple models that predict the mechanical stresses experienced by whales during collisions. By comparing our predictions with published models and with data from ship strikes on various whale species, we developed a model for lethal injury as a function of several vessel and whale properties, finding that collisions that create stresses in excess of 0.241 MPa were likely to cause lethal injuries to large whales. Furthermore, this model has revealed that (1) vessels of all sizes can yield stresses higher than this critical level, and (2) large vessels produce stresses much larger than this even when travelling at reduced speeds (i.e., 10 knots). The model is fast enough to power an interactive GUI‐based tool (in R) and flexible enough to simulate strikes by vessels of different masses and speeds upon whales of different species, sizes, and physical conditions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.003
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 head0.111
GPT teacher head0.322
Teacher spread0.210 · 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

Citations33
Published2020
Admission routes1
Has abstractyes

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