Evaluating the Sampling Design of a Long-Term Community-Based Estuary Monitoring Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Community-based monitoring programs (CBMPs) are a cost-effective option to collect the long-term data required to effectively monitor estuaries. Data quality concerns have caused some CBMP datasets, which could fill knowledge gaps for aquatic ecosystems, to go unused. The Community Aquatic Monitoring Program (CAMP) is a CBMP that has collected littoral nekton assemblage data from estuaries in the southern Gulf of St. Lawrence since 2003. Concerns with the CAMP sampling design (station placement and numbers) have prevented decision-makers from using the data to inform estuary health assessments. This study tested if CAMP’s sampling design that accommodates volunteer participation provides similar information as a scientific sampling approach. Six CAMP stations and six stations selected using a stratified random design were sampled at ten estuaries. A permutational-MANOVA revealed nekton assemblages were generally not significantly different between the two sampling designs. The current six CAMP stations are sufficient to detect the larger differences in species abundances that may indicate differences in estuary condition. The predicted increase in precision (2%) with twelve stations is not substantive enough to warrant an increased sampling effort. CAMP’s scientific utility is not limited by station selection bias or numbers. Furthermore, well-designed CBMPs can produce comparable data to scientific studies.
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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) | 0.001 | 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 it