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Record W4414867569 · doi:10.1111/tgis.70120

<scp>GeoSimulations</scp> in <scp>4D</scp> : Multi‐Dimensional Agent‐Based Model for Representing Dynamics of Southern Resident Killer Whales

2025· article· en· W4414867569 on OpenAlexafffund
Alex K. Smith, Suzana Dragićević

Bibliographic record

VenueTransactions in GIS · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser University
KeywordsEndangered speciesChinook windMarine conservationWhaleMarine protected areaMarine fishMarine life

Abstract

fetched live from OpenAlex

ABSTRACT The Southern Resident killer whales (SRKW) are an endangered species that occupy marine environments on the west coast of North America, including the Salish Sea. Representing marine species in this environment can be achieved by multidimensional space–time simulation modeling that provides more information about locations and behaviors for use in protection and preservation strategies. In this study, the main objectives are: (a) design and implement a prototype four‐dimensional (4D) agent‐based model (ABM) to simulate marine species, (b) implement the model to represent SRKW's J Pod in the Salish Sea, and (c) investigate their 3D movements considering bathymetry, vessel noise, and prey–predator relationships with Chinook salmon. The simulation results indicate the SRKW are often present around the San Juan Islands, and the model locations correlate with real data on reported sightings, prey availability, and low vessel noise. The developed methodology can be used to further inform and improve marine conservation and management practices.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.031
GPT teacher head0.281
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2025
Admission routes2
Has abstractyes

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