The NSERC Canadian Lake Pulse Network: A national assessment of lake health providing science for water management in a changing climate
Bibliographic record
Abstract
The distribution and quality of water resources vary dramatically across Canada, and human impacts such as land-use and climate changes are exacerbating uncertainties in water supply and security. At the national level, Canada has no enforceable standards for safe drinking water and no comprehensive water-monitoring program to provide detailed, timely reporting on the state of water resources. To provide Canada's first national assessment of lake health, the NSERC Canadian Lake Pulse Network was launched in 2016 as an academic-government research partnership. LakePulse uses traditional approaches for limnological monitoring as well as state-of-the-art methods in the fields of genomics, emerging contaminants, greenhouse gases, invasive pathogens, paleolimnology, spatial modelling, statistical analysis, and remote sensing. A coordinated sampling program of about 680 lakes together with historical archives and a geomatics analysis of over 80,000 lake watersheds are used to examine the extent to which lakes are being altered now and in the future, and how this impacts aquatic ecosystem services of societal importance. Herein we review the network context, objectives and methods.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".