Residue of PCB and Organochlorine Pesticides in Fish from Lakes and Rivers in the World (II)
Bibliographic record
Abstract
Residue of PCB, DDT and HCH in fish from lakes and rivers in the world was reviewed from the surveys in the years of 1995-2007. PCB and T-DDT concentrations in the fish from the lakes and rivers of America were relatively high, but those of Europe, Asia and Africa were relatively low. T-HCH concentrations in the fish were relatively low in both of the lakes and rivers in all the world. DDT was presumed to be used in Egypt, Tanzania and Brazil from the high percentage of pp’-DDT in the composition of T-DDT in the several kinds of fish from Lake Burullus, Lake Victoria and Ponta Grossa Lake. Technical HCH was presumed to be used in Japan, China and India from the low percentage of γ-HCH in the composition of T-HCH in the lake and the river fish in the countries. On the contrary, Lindane was presumed to be used in the countries of Europe and Africa from the high percentage of γ-HCH in the lake and the river fish in the countries. Half-lives (t1/2) of PCB and T-DDT in fish from lakes in Japan, USA and Sweden were calculated from the long-term monitoring data using an exponential decay model. The t1/2 values were 20years in Lake Biwa, 10years in Lake Ontario, 7years in Lake Michigan and 20years in Lake Storvindeln for PCB and 9years in Lake Biwa, 10years in Lake Ontario, 8years in Lake Michigan and 7years in Lake Storvindeln for T-DDT.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 0.000 |
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".