Oil And Gas Development And the Potential For Contamination of Moose (Alces Alces) In Northeast BC
Bibliographic record
Abstract
Abstract We analyzed moose tissue samples for extractable petroleum hydrocarbons (EPH), heavy metals and polycyclic hydrocarbons (PAH) and compared levels of concentration in tissues between two areas with noticeably different levels of oil and gas activity. Our treatment area (extensive oil and gas activity) had 135 oil and gas wells in a 2,100 km2 area in 2003 and the control area (2,900 km2) had no active oil and gas wells in 2003. EPH analysis showed the treatment area had significantly higher levels of C32, C33, C37 and C38 than the control area. Significantly higher levels of heavy metal concentrations in 14 of 28 heavy metals tested were found in tissues sampled from the treatment area. No significant difference in PAH were found between the study areas. This study suggests that oil and gas activity may have negative impacts on the health of moose in Northeast BC. In order to quantify this impact, further work needs to be undertaken that will monitor moose activities as they relate to landscape feature availability to determine the use and impact of oil and gas facilities on the health of moose in Northeast BC. Introduction First Nations of Northern Canada today remain reliant on their traditional sources of food for meat, berries and fish. Much of the meat protein that is consumed by First Nations is obtained by hunting and trapping in traditional areas. First Nations people of the Northwest Territories have relied on moose (Alces alces) as a traditional food source for thousands of years(1), as many First Nations people have, including the communities of Northeast British Columbia. Moose meat is the most sought after meat by the West Moberly and Saulteau First Nations of Northeast British Columbia. As the level of oil and gas exploration and development activity in northeastern British Columbia has increased, concerns regarding the consumption of contaminated meat have increased. Oil and gas activity releases contaminants in the form of heavy metals, polycyclic aromatic hydrocarbons (PAH), extractable petroleum hydrocarbons (EPH) and volatile by-products of oil and gas to the water, soil and air during production, processing, storage and distribution(2). These contaminants may become bio-magnified or may bio-accumulate in the environment through predation and consumption of plants and animals. Contamination of wild game from industrial activity has been documented in the past(3–5). Indian and Northern Affairs Canada issued a fact sheet in 2003(1) that suggested contaminants in moose (cadmium) resulted from naturally occurring cadmium up-take during plant growth, and that these plants, when consumed by moose, will transfer this contaminant to tissues and organs. Cadmium is only one such heavy metal that may contaminate large mammals such as moose. Heavy metals occur naturally, and can enter aquatic ecosystems by anthropogenic activities such as mining, burning, deforestation, agriculture and urban activity(4). Consumption by wildlife of heavy metals that enter surface and subsurface aquatic ecosystems occurs when the metals are transferred to animals either directly by consumption of water, or indirectly through the uptake by plants and the eventual consumption of contaminated plants by wildlife.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".