Indicator: Hexachlorobenzene Levels in Herring Gull Eggs from the Great Lakes
Bibliographic record
Abstract
The Great Lakes compose an important and unique ecosystem. They represent the largest system of fresh water in the world and provide many economic and ecological benefits to the surrounding areas. The Great Lakes basin, which includes the lakes and over 290,000 square miles of land that drains into them, supports concentrated industry and agriculture for the U.S. and Canada. These activities have taken their environmental toll on the Great Lakes as sewage, fertilizer and pesticide run-off, and industrial wastes have deteriorated water quality. In response to this, there have been many pollution prevention and clean-up efforts sponsored by local governments, the EPA, and the Canadian government. Long-term monitoring is necessary to track the progress of these initiatives and to prevent any further degradation of the Great Lakes ecosystem. This indicator measures hexachlorobenzene (HCB) levels in herring gull eggs from each of the Great Lakes. Past uses of HCB include its use as a fungicide, in making ammunition and fireworks, and in manufacturing synthetic rubber. HCB is a persistent, bioaccumulative, and toxic (PBT) pollutant targeted by the EPA. Thus, it is well suited for long-term ambient monitoring. It is important to track the levels of HCB in herring gull eggs because it has been linked to harmful effects in birds and other wildlife. Due to the nature of this chemical, its toxicity to wildlife and humans, and the status of the herring gull as a major indicator species for the Great Lakes, this indicator provides a good measure of the environmental quality of the Great Lakes ecosystem. The chart displays the HCB levels in herring gull eggs at sampled sites from each of the Great Lakes from 1977 to 1996. • Over the past 20 years, levels of HCB in herring gull eggs have dropped considerably from an average of 0.4 ppm in 1977 to 0.04 ppm in 1996. • Since the mid-1980’s, the levels of HCB in herring gull eggs have been similar across all of the Great Lakes. Notes: Parts per million in whole egg samples, wet weight. For Lake Michigan in
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".