RESEARCH ARTICLE Attention-deficit/hyperact a
Bibliographic record
Abstract
DOI 10.1186/s12887-015-0368-xheavy metals and organic contaminants in workplaceMedical College, Shantou, Guangdong, People’s Republic of China Full list of author information is available at the end of the articleElectronic waste (e-waste) is the most rapidly growing waste problem in the world. But to date, consumers, in-dustry and government have only taken small steps to deal with this looming problem. Furthermore, some de-veloped countries such as United States, Canada, Japan, and European countries that generate overwhelming ma-jority of the hazardous waste have made use of exporting the e-waste crisis to the developing countries of Asia [1,2]. situated in the southern coast of China, is one of the lar-gest e-waste destinations in the world. It has a total area of 52 km2 with a population of 133,000 (in 2008). Nearly 60–80 % of families in the town are engaged in e-waste recycling operations. The hazardous recycling methods are mainly as follows: sorting, firing, incinerating, acidic/ alkaline bathing, manual disassembling, open burning of wires and cables and strong acid leaching [3]. By such primitive means, approximately 1.7 million tons of e-waste are dismantled annually, threatening the local environ-ment and resident health. Several studies have reported that the Guiyu environment has soaring levels of toxic
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.349 | 0.068 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".