Methemoglobinemia Risk Factors: Response to Avery and L'hirondel
Bibliographic record
Abstract
The article by Knap et al., “Indicators ofOcean Health and Human Health:Developing a Research and MonitoringFramework” (Knap et al. 2002), was awelcome overview of issues that link theenvironmental condition of marine/oceanecosystems and human disease. The comple-ment to the growing concern about theconnection between health and the marineenvironment is a corresponding emphasison large freshwater lake ecosystems andhuman health.In the United States and Canada, forexample, the Great Lakes basin contains a setof inland seas that are oceanographic in scale.They serve as a highway for internationalmaritime commerce and support a $1 bil-lion/year recreational and commercial fishingindustry. In addition, they must also supplydrinking water for over 15 million people. The Great Lakes hold about 20% of theworld’s surface freshwater. In this context,the degradation of the Great Lakes ecosys-tem through chemical and biological conta-mination presents an enormous challenge forthe future. Questions about the impact ofmethyl mercury, polychlorinated biphenyls,and other chemicals on the health of thosewho eat fish from the Great Lakes; about therole of bacterial loading of coastline beacheson disease; and about the quality of drinkingwater taken from the lakes are among thosein need of intense study. Surprisingly, in comparison with thenumber of research organizations and fund-ing opportunities that concentrate on themarine environment, there are very fewgovernmental or academic programs thattarget the Great Lakes environment. In thiscontext, it should be a priority to developresearch programs that can enlarge theknowledge base so that the Great Lakes canbe sustained as the centerpiece of our fresh-water resources.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".