Sources and mechanisms of delivery of E. coli (bacteria) pollution to the Lake Huron shoreline of Huron County
Bibliographic record
Abstract
In 2004, the Lake Huron Science Committee (LHSC) was initiated by Ministry of the Environment (MOE), in consultation with the Ontario Ministry of Agriculture and Food (OMAF) and Environment Canada (EC), in response to public concerns over bacterial (E. coli) pollution along the southeast shoreline of Lake Huron. The committee was created as an MOE-led, multi-agency technical committee to: i) identify sources of E. coli (as an indicator of fecal pollution) to the shoreline of Lake Huron, ii) investigate the extent of influence of fecal pollution sources on the shoreline, and, iii) make recommendations on possible actions to address the microbial pollution of fecal origin. The approach was to develop a time-limited science work plan, and to identify partners to conduct the necessary monitoring and analysis for the identification of E. coli contamination and the associated fecal pollution sources. The LHSC adopted a phased strategy: Phase 1: Review and Synthesis of Existing Information; Phase 2: Development of Additional Studies; Phase 3: Development of Recommendations. This interim report presents the progress of the committee on Phase 1. The report presents a summary of existing information and attempts to provide a synopsis of the state of understanding of microbial pollution of fecal origin on the Huron County shores of Lake Huron, as inferred primarily from results for the fecal pollution indicator species E. coli. The approach taken was to examine information from three perspectives: i) the potential sources of microbial pollution of fecal origin to the shoreline were identified and described, ii) evidence for impacts on the shoreline by the various potential sources was reviewed, and iii) the potential mechanisms of delivery of microbial pollutants to the shoreline were explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".