Turkey Lakes Watershed, Ontario, Canada: 40 years of interdisciplinary whole-ecosystem research
Bibliographic record
Abstract
IntroductionThe Turkey Lakes Watershed (TLW) study (https://www.canada.ca/en/environment-climate-change/services/turkey-lakes-watershed-study.html) was established in 1979 and is one of the longest running watershed-based ecosystem studies in Canada (Foster, Beall & Kreutzweiser, 2005; Jeffries, Kelso & Morrison, 1988; Morrison, Cameron, Foster & Groot, 1999). The watershed drains 10.5 km2 of Eastern Temperate Mixed Forest (Baldwin et al., 2018) or Great Lakes – St. Lawrence forest region (Rowe, 1972) within the Boreal Shield Ecozone (Wiken, 1986), and is located approximately 60 km north of Sault Ste. Marie, Ontario (47°03’N, 84°25’W) (Figure 1). Researchers from several federal government departments (Natural Resources Canada (NRCAN), Environment and Climate Change Canada (ECCC) and Fisheries and Oceans Canada (DFO) established this research watershed to evaluate the impacts of acid rain on terrestrial and aquatic ecosystems (e.g., Foster, Hazlett, Nicolson & Morrison, 1989; Hazlett, Curry & Weldon, 2011; Jeffries, Semkin, Beall & Franklyn, 2002; Kelso 1988). Since its inception, many studies have taken a multi-disciplinary, whole-ecosystem approach to investigate the processes governing terrestrial and aquatic responses to natural and anthropogenic disturbances. This holistic approach has allowed research to expand from its original acidification focus to address a range of other ongoing and emerging environmental issues (e.g. habitat alteration, organic contaminants, forest management, climate change) and to involve numerous academic, government and industrial collaborators.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.029 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".