River Guardians Water Quality Monitoring Program
Bibliographic record
Abstract
The Annapolis River Guardian volunteers began collecting water quality data in the Annapolis River watershed in 1992. The Clean Annapolis River Project (CARP) initiated the program as a public awareness project, and has had numerous volunteers collecting samples over the years. It is one of the longest running and most extensive volunteer based water quality programs in Eastern Canada. It is also CARP’s longest running project. At least 100 volunteers from the Annapolis Valley community have participated in the program over the years, and over 4,000 water samples have been collected and analyzed. The program was initiated in the early 1990’s by Dr. Graham Daborn and Dr. Mike Brylinsky of the Acadia Centre for Estuarine Research (ACER). Many groups were involved in the planning process for the program, including staff from the Nova Scotia Department of Health, the Nova Scotia Department of Environment, Nova Scotia Community College, and CARP. Some modifications have been made over the years, but the core has remained the same. Since the early 90’s, 8 sites have been actively monitored. Sampling occurs from late spring to late fall, biweekly. Water quality data is collected through a water quality instrument, otherwise known as a YSI, and grab samples are sent for laboratory analysis to determine E.coli levels in the River. The information is then analyzed by CARP staff with this historical data, which is then later produced into a report card looking at water quality status and trends in the watershed over time. See past report cards here: https://www.annapolisriver.ca/annapolis-river-guardians.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.006 |
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".