Trophic triage: a tiered eutrophication vulnerability screening tool for lakes in sparsely monitored regions
Bibliographic record
Abstract
Johnston L, Hiscock A, Holmes B, Bermarija T, Scott R, Sinclair A, Jamieson R. 2020. Trophic triage: a tiered eutrophication vulnerability screening tool for lakes in sparsely monitored regions. Lake Reserv Manage. XX:XXX–XXX. Eutrophication and the occurrence of harmful algal blooms (HABs) have been observed in lakes throughout North America. Here, we developed a high-level lake screening tool for characterizing eutrophication vulnerability in sparsely monitored regions. The screening tool involves a desktop study (Tier 1) to classify the vulnerability of lakes to eutrophication as either low, moderate, or high vulnerability. A subset of lakes is then identified from this initial assessment for a preliminary water quality sampling program to confirm the desktop evaluation (Tier 2). From this evaluation, lakes in a final subset undergo a comprehensive sampling program to establish final vulnerability levels (Tier 3). The screening tool was initially developed and demonstrated for lakes within the Municipality of Cumberland County, Nova Scotia, Canada. Five lakes, spanning a range of land uses, morphologies, and watershed settings, were subjected to a detailed water quality monitoring program to help refine factors and thresholds in the screening tool. Tier 2 and Tier 3 were then applied to the 5 study lakes to demonstrate the complete screening process. Tier 1 of the screening tool was further validated on an additional 29 lakes in Nova Scotia, and performed as intended for the majority of lakes, predicting the same or higher trophic state than the one currently measured for 25 of the 29 lakes. For the 4 lakes with trophic states that were underpredicted, the vulnerability level was still correctly predicted. The screening tool proved to be a robust approach for identifying lakes that are vulnerable to eutrophication, and for prioritizing monitoring activities.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".