MétaCan
Menu
Back to cohort
Record W2148633364 · doi:10.1002/jwmg.259

Time geography and wildlife home range delineation

2011· article· en· W2148633364 on OpenAlexaff
Jed Long, Trisalyn Nelson

Bibliographic record

VenueJournal of Wildlife Management · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHome rangeWildlifeRange (aeronautics)GeographyContext (archaeology)Time geographyMeasure (data warehouse)Movement (music)Computer scienceEnvironmental resource managementData scienceEcologyEnvironmental scienceHuman geographyData miningHabitatEngineeringEconomic geographyHistorical geographyBiology

Abstract

fetched live from OpenAlex

Abstract We introduce a new technique for delineating animal home ranges that is relatively simple and intuitive: the potential path area (PPA) home range. PPA home ranges are based on existing theory from time geography, where an animal's movement is constrained by known locations in space–time (i.e., n telemetry points) and a measure of mobility (e.g., maximum velocity). Using the formulation we provide, PPA home ranges can be easily implemented in a Geographic Information System (GIS). The advantage of the PPA home range is the explicit consideration of temporal limitations on animal movement. In discussion, we identify the PPA home range as a stand‐alone measure of animal home range or as a way to augment existing home range techniques. Future developments are highlighted in the context of the usefulness of time geography for wildlife movement analysis. To facilitate the adoption of this technique we provide a tool for implementing this method. © 2011 The Wildlife Society.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.196
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), 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

Citations56
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Wildlife ManagementSame topicWildlife Ecology and ConservationFrench-language works237,207