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Record W6888862654 · doi:10.23719/1530585

Data Links for Monitoring Chemical Contaminants in the Gulf of Maine, using Sediments and Mussels: an evaluation

2024· dataset· en· W6888862654 on OpenAlexaboutno aff

Bibliographic record

VenueEnvironmental Protection Agency (EPA) Repository · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSedimentMusselEcosystemDocumentationEnvironmental monitoringGeneral partnershipWater quality

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.098
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0130.013
Science and technology studies0.0020.001
Scholarly communication0.0040.008
Open science0.0030.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0110.007

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.

Opus teacher head0.080
GPT teacher head0.332
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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