Data Links for Monitoring Chemical Contaminants in the Gulf of Maine, using Sediments and Mussels: an evaluation
Bibliographic record
Abstract
The monitoring programs used to determine if sediments and mussels can be used interchangeably to assess environmental condition were: Mussel Watch (MW, National Status and Trends (NS&T), NOAA), Gulfwatch (GW, Gulf of Maine Council), National Coastal Assessment (NCA, US EPA), National Coastal Condition Assessment (NCCA, US EPA), and newly available sediment data from the EcoSystem Indicator Partnership (ESIP). Mussel Watch, Gulfwatch, NCA and NCCA are high profile, long-term, well organized national and regional mussel and/ or sediment monitoring programs. Sediment contaminant data generated by Eastern Charlotte Waterways (ECW) Inc, from funding by the Gulf of Maine Council on the Marine Environment’s EcoSystem Indicator Partnership, were used for sites in the Canadian portion of the GOM; sediments were collected by ECW and analyzed by RPC (http://www.rpc.ca/english/index.html). All the data source programs have quality assurance measures (details can be found at the URLs listed below). NOAA’s NS&T Mussel Watch (NOAA MW) https://en.wikipedia.org/wiki/Mussel_Watch_Program https://products.coastalscience.noaa.gov/nsandt_data/data.aspx Gulf of Maine Council’s Gulfwatch (GOMC GW) http://www.gulfofmaine.org/2/gulfwatch-homepage/ https://gulfofmaine.org/public/gulfwatch-contaminants-monitoring/data-reports/ The Canadian portion of Gulfwatch also has published QA documentation (Sowles et al., 1997). EPA’s National Coastal Assessment (EPA NCA) & National Coastal Condition Assessment (NCCA) https://archive.epa.gov/emap/archive-emap/web/html/about.html https://www.epa.gov/national-aquatic-resource-surveys/ncca https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource- surveys EcoSystem Indictor Partnership (ESiP) http://www.gulfofmaine.org/2/esip-homepage/ Eastern Charlotte Waterways Inc. (D. Killorn, pers comm). Sediments were collected by ECW and analyzed through a subcontract to RPC. Data with accompanying QA data were obtained from Donald Killorn, ECW (pers comm).
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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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".