Implications of linear developments on northern fishes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".