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Record W2498351905 · doi:10.22230/jem.2016v16n1a585

Managing Zone-of-Influence Impacts of Oil and Gas Activities on Terrestrial Wildlife and Habitats in British Columbia

2016· article· en· W2498351905 on OpenAlexfundaboutno aff
Steven F. Wilson

Bibliographic record

VenueJournal of Ecosystems and Management · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsnot available
FundersBC Oil and Gas Research and Innovation Society
KeywordsWildlifeHabitatAbiotic componentEnvironmental scienceEcosystemStressorEcologyGeographyEcological footprintTerrestrial ecosystemEnvironmental resource managementEnvironmental protectionBiologySustainable development

Abstract

fetched live from OpenAlex

A “zone of influence” is the difference between an anthropogenic activity’s spatial footprint and the extent of the activity’s effects on surrounding habitat and wildlife. This article reviews studies that have measured zones of influence for site-level activities that are relevant to oil and gas activities in British Columbia in order to inform the development of policies and procedures to manage their effects on terrestrial habitats and wildlife. Creation of edges, as well as noise and activity associated with industrial sites and roads, are the major stressors that generate zones of influence. These stressors create cascading effects that can result in altered ecosystems through a variety of mechanisms. Stressors can create abiotic and floristic effects that generally extend < 100 m into surrounding intact habitat, but effects on wildlife can extend up to 5 km and sometimes farther. Mitigating stressors at their source should reduce zones of influence and the need to apply management buffers to separate industrial activities from ecological resources.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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