Patterns of microbial contamination on Northumberland Strait shores
Bibliographic record
Abstract
The re-emergence of episodic faecal contamination of Parlee and Murray Corner beaches, on the Northumberland Strait of New Brunswick, Canada, in 2017, raised renewed community concerns on the health, environmental and tourism sustainability of these community resources, and led to creation of an Integrated Watershed Management Plan for the Shediac Bay Watershed (October 2021). In response we have to date compiled, curated and made accessible 205,772 microbial water quality data records spanning over 80 years from Southeastern New Brunswick and the Northumberland Strait. This dataset derives in large part from Shellfish Surveys completed by Environment and Climate Change Canada, along with data generated by multiple government agencies, Non-Governmental Organizations and citizen science sources. Records derived from these multiple sources are now deposited in the Gordon Foundation's DataStream (https://atlanticdatastream.ca), an open access common platform for sharing structured information on fresh and marine water health, delivered on a pan-Canadian scale, in collaboration with regional monitoring networks. We herein outline our data assembly, curation and deposition, along with preliminary analyses of contamination patterns at three representative sites on the Northumberland Strait coast of New Brunswick. Our results suggest that cumulative rainfall over 48 h is useful in predicting contamination risk at the developed Parlee Beach, and thereby demonstrate how open data can be used to inform policy and management decisions.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".