Post-Pollution: Characterizing Ecological Recovery in a Historically Nutrient Enriched Lake.
Bibliographic record
Abstract
An iconic story of recovery from nutrient pollution is the restoration of the heavily enriched Lake Washington in Seattle, Washington State. Originally an integral part of the municipal septic system, a diversion of wastewater in 1968 has allowed Lake Washington to return to what has been recently described as a natural and healthy state. Yet is it accurate to characterize a lake as “recovered” based purely on chemical measurements? Does a legacy of pollution linger on in the ecology of a lake system long after the lake has been given a clean bill of health?Using paleolimological reconstructive techniques it is possible to compare pre-pollution and post-pollution communities of algae by looking at microfossils stored chronologically in the lake-bottom sediment. Use of this technique has afforded a test of the assumption that once pollution stress in a lake is alleviated, the algal communities quickly return to the pre-pollution state. Work on Lake Washington indicates that this does not always hold true. Instead, it suggests that a legacy of pollution persists in the algae and ecological community of the lake long after the nutrient levels have returned to normal.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".