Elucidation of ecosystem attributes of two Mackenzie great lakes with trophic network analysis
Bibliographic record
Abstract
The Mackenzie Basin in northwestern Canada is a high-latitude region, with one of the largest watersheds in the world. The Mackenzie great lakes, consisting of Great Bear Lake, Great Slave Lake and Lake Athabasca form the large lake complex. The human presence in the area is small in terms of population and industry and thus these ecosystems remain comparatively pristine and show no major changes in the fish communities. Ecopath with Ecosim (EwE), the most important and most used ecosystem trophic network modelling tool to study the ecosystem-level responses to changes, and information available in the scientific literature together with traditional knowledge about Great Slave Lake and Great Bear Lake was used to elucidate the ecosystem attributes. Our models give a cohesive view of these two ecosystems that will allow researchers and decision makers to explore questions regarding the stability of fisheries and future ecological change. The moderate trophic level of fish catch along with the small percentage of primary production required to sustain fisheries in both lakes demonstrated that fisheries were sustainable during the time period modelled. The ecosystem indices and attributes of the comparatively pristine Mackenzie great lakes were compared with those of two Laurentian Great Lakes having similar types of Ecopath ecosystem models. The metrics utilized to assess comparatively the ecosystem's maturity, stability and health indicated a decline in ecosystem maturity and stability from pristine Great Bear Lake to transitioning Lake Ontario.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".