A social–ecological geography of southern Canadian lakes
Bibliographic record
Abstract
Anthropogenic pressures, including urban and agricultural expansion, can negatively influence a lake's capacity to provide aquatic ecosystem services (ES). However, identifying lakes most at risk of losing their ES (i.e., higher vulnerability) requires integrating information on lake ecological state, global change threats, and ES use. Here, we provide a social–ecological framework that combines these features within a regional context by evaluating the ecological state of 659 lakes across Canada. Using the deviation of impacted lakes from reference ones, we identified much higher total nitrogen and chloride concentrations as the main indicators of an altered lake ecological state in all regions identified. Lake ecological state was mapped using an additive colour model along with regional scores of threat levels and recreational ES use. Urban and agriculturally developed areas were linked to higher lake vulnerability and ES loss. Lakes in Southern Ontario were most concerning, being highly altered, under threat, and heavily used. Lakes near coastal urban centers were altered and used, but less threatened, whereas those in the Prairies were altered and threatened, but less used. Our novel framework provides the first social–ecological geography of Canadian lakes, and is a promising tool to assess lake state and vulnerability at scales relevant for management.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".