Bibliographic record
Abstract
Conducted by a committee of Canadian and US government and university scientists, Gulfwatch examines the effects of decades of development and industrialization on the water quality of the Gulf as it relates to human health as well as its impact on other marine organisms. Gulfwatch scientists collect blue mussels at over 60 US and Canadian sites Gulf-wide, and analyze the organisms’ tissue for potentially harmful levels and concentrations of toxins including heavy metals, chlorinated pesticides, polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs). New Hampshire increased the number of Gulfwatch sampling locations from two sites per year in 1997 to an average of five sites per year from 1998-2005. The increased spatial coverage provides comprehensive information for contaminant concentrations throughout the New Hampshire estuarine waters. All samples collected for the Gulfwatch monitoring program, from the Canadian provinces as well as the New England states involved, have been sent to the same laboratories for analysis. All of the samples have been analyzed at the same time in the same laboratories in an effort to reduce error and variability. This practice has ensured the consistency that was necessary to generate an accurate overall picture of the health of the Gulf. During the 2005 sampling season, mussels were collected at 4 sampling locations in New Hampshire. Oyster and clams were collected at one station each. The Gulf of Maine Council covered the costs for analyzing two mussel tissue samples. The NHEP covered the costs for analyzing the remaining two mussel samples plus one sample of oyster tissue and one sample of clam tissue. The Gulfwatch tasks from the 2005 agreements between DES and the NHEP are listed below.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".