The large lake ecosystems of northern Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The Great Lakes of northern Canada are relatively understudied ecosystems in comparison to the better-investigated Laurentian Great Lakes. This chain of lakes extends north from Lake Winnipeg (a shallow prairie lake) to Wollaston Lake and Lake Athabasca (moderately deep arboreal lakes) to Great Slave Lake (a deep subarctic lake) to Great Bear Lake (a deep lake located in the Arctic Circle). Many of these lakes have experienced minor localized anthropogenic impacts. Impacts include mining and fishing in the north and agricultural and urban impacts in the south. While most of these lakes are located in the relatively undeveloped regions of Canada, the northward migration of natural resource-based industries such as forestry, mining, agriculture and oil and gas operations may potentially affect their ecosystem health. Research programs are required to better understand the natural features of these ecosystems to further protect them from anthropogenically driven change. Long-term monitoring programs are also required to protect fish, water quality and other ecosystem features. An emerging problem is meeting northern community concerns with environmental protection while providing the economic base for an increasingly modern lifestyle.
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.
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 | 0.001 | 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 | 0.001 | 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 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it