Widespread detection of human- and ruminant-origin Bacteroidales markers in subtidal waters of the Salish Sea in Washington State
Bibliographic record
Abstract
Rising populations around coastal systems are increasing the threats to marine water quality. To assess anthropogenic fecal influence, subtidal waters were examined monthly for human- and ruminant-sourced Bacteroidales markers at 80 sites across six oceanographic basins of the Salish Sea (Washington State) from April through October, 2011. In the basins containing cities with individual populations>190,000, >50% of sites were positive for the human marker, while in the basins with high densities of dairy and cattle operations, ∼30% of sites were positive for the ruminant marker. Marker prevalence was elevated in spring (April and May) and fall (October) and reduced during summer (June through September), corresponding with seasonal precipitation. By logistic regression, the odds of human marker detection increased with percentage of adjacent catchment impervious surface, dissolved nitrate concentration, and abundance of low nucleic acid bacteria, but decreased with salinity and chlorophyll fluorescence. The odds of ruminant marker detection increased with dissolved ammonium concentration, mean flow rate for the nearest river, and adjacent shoreline length. These relationships are consistent with terrestrial to marine water flow as a transport mechanism. Thus, Bacteroidales markers traditionally used for identifying nearby sources can be used for assessing anthropogenic fecal inputs to regional marine ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".