Bacterial Source Tracking in Pathogen TMDL Development and Implementation Part II: Challenge and Opportunity
Bibliographic record
Abstract
Bacterial Source Tracking in Pathogen TMDL Development and Implementation Part II: Challenge and OpportunityPathogen contamination is among the leading causes of water quality impairment nationwide. Despite advanced sanitation conditions, contaminated waters continue to cause illness through drinking water use, recreational water use and shellfish harvesting. Knowing the sources of waterborne pathogens in an impaired watershed is of great value in analyzing the potential risk of transmission of...Author(s)Harry X. ZhangJoseph T. MauroLauren A. FillmoreJames WheelerSourceProceedings of the Water Environment FederationSubjectSession 8 - Surface Water Quality and Ecology: TMDLsDocument typeConference PaperPublisherWater Environment FederationPrint publication date Jan, 2003ISSN1938-6478SICI1938-6478(20030101)2003:12L.850;1-DOI10.2175/193864703784755049Volume / Issue2003 / 12Content sourceWEFTECFirst / last page(s)850 - 868Copyright2003Word count221
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".