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Record W1964456641 · doi:10.1139/er-2014-0075

Implications of linear developments on northern fishes

2015· article· en· W1964456641 on OpenAlexaffvenueabout
Peter A. Cott, Allison Schein, Bruce W. Hanna, Tom A. Johnston, Donald D. MacDonald, John M. Gunn

Bibliographic record

VenueEnvironmental Reviews · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsLaurentian UniversityVancouver Island UniversityWilfrid Laurier UniversityGovernment of Northwest Territories
Fundersnot available
KeywordsHabitatEcosystemProductivityPipeline transportEnvironmental scienceGeographyFisheryEnvironmental protectionEnvironmental resource managementEcology

Abstract

fetched live from OpenAlex

Canada’s Northwest Territories (NWT) is currently the focus of significant exploration and development activity. In particular, increased global demand for oil and gas resources has resulted in an escalation in the search for hydrocarbon deposits. Canada’s north is a landscape defined by water where large numbers of pristine water bodies still exist in remote areas. Northern development activities conducted in these areas will affect these sensitive aquatic ecosystems that support important fish and fish habitat. Fishes in low productivity northern systems grow slowly and mature late, making them particularly sensitive to environmental perturbations. The fishery resources of the NWT are an integral component of our northern ecosystems, and are of significant economic and cultural importance to northern people. By necessity, linear developments constructed in the NWT, such as roads, seismic lines, and pipelines, intersect lakes, rivers, and streams. This paper discusses linear development activities and their impacts on northern fishes, with a focus on oil and gas developments. Once a target area is identified, the development of northern oil and gas reserves typically follows a predictable sequence of events: (i) construction of temporary access roads into the exploration area to conduct seismic surveys to delineate reserves; (ii) exploration well(s) are drilled to assess the potential of the deposit; (iii) if the deposit is of economic interest, then production wells are developed and gathering systems constructed, often coupled with additional transportation infrastructure; (iv) a pipeline is then built to move the hydrocarbons southward to processing facilities; and (v) after the reserve is depleted, closure of all associated infrastructure is conducted and the site is remediated. The main stressors from these activities that may impact fish and aquatic ecosystems include sediment transport to water bodies, noise and pressure impacts from the use of explosives, water withdrawal, obstructions to flow and fish passage, removal of in-stream structure and riparian vegetation, enhanced access and fisheries exploitation, and contaminant spills. These stressors can adversely affect fish directly (e.g., through direct toxicity associated with exposure to elevated contaminants) or indirectly (e.g., through habitat degradation). Such impacts on fish can vary in severity, and on temporal and spatial scales, depending on the nature and extent of the disturbance. These activities can have cumulative impacts and can be exacerbated by natural or indirect stressors, such as a changing climate or forest fires. Appropriate baseline monitoring needs to be conducted, prior to development, to allow for appropriate mitigation to be employed and sound and responsible resource management decisions to be made within an adaptive management framework.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.663
Threshold uncertainty score0.679

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.066
GPT teacher head0.277
Teacher spread0.211 · 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
GenreReview

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
Published2015
Admission routes3
Has abstractyes

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