Johnson and Vallentyne’s legacy: 40 years of aquatic research at the Experimental Lakes Area
Bibliographic record
Abstract
Wally Johnson and Jack Vallentyne played key roles in the establishment of the Experimental Lakes Area (ELA), which comprises a research team, a set of protected lakes, and a field station, with the mandate to quantify anthropogenic impacts to lakes through whole-ecosystem manipulation and monitoring. We begin this collection of papers, celebrating four decades of aquatic research at the ELA, by reflecting on the historical relevance and scientific milestones of the ELA. The remaining papers encompass themes at the core of ELA research: long-term ecological monitoring of unimpacted reference lakes, ecosystem responses to anthropogenic stressors through whole-system experimentation, recovery of manipulated ecosystems from perturbation, and detailed mechanistic studies. Utilizing these approaches, papers in this issue examine a wide variety of anthropogenic impacts on freshwater including the ecosystem effects of climate change, recovery from lake acidification, upland and wetland flooding on methyl mercury levels in biota, endocrine-disrupting chemicals on fish populations, and freshwater aquaculture. These studies emphasize the value of long-term monitoring and experimentation at the ecosystem scale for understanding human impacts on freshwaters.
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".