Overview of sources, fate, and Impact of Endocrine Disrupting Compounds in environment and assessment of their Regulatory Policies across different Continents
Bibliographic record
Abstract
Under the present research we have reviewed the sources, fate of Endocrine Disrupting Compounds (EDCs) and its impact on the health of both human and the environment. Followed by this we examine the regulatory frameworks and policies from different continents across the world to identify those with the capacity to address EDCs. Data derived from experiments and epidemiological studies of EDCs demonstrated the negative impact of EDCs on organisms like humans and other animals even at very low concentration ranging from nano to micro grams per liter. As a result of a lack of efficient management and remediation operations these compounds are increasing consistently into the environment. Furthermore, a critical examination of the existing legal framework regarding use of EDCs revealed the presence of weak, vague and insufficient regulations worldwide. It also revealed that most of the substantial rules, regulation and legal framework are available only in developed nations like USA, Canada, Australia, Japan, South Korea and different countries of European Union. Handful information has been developed in some nations with transitional economies; essentially little or no information on EDCs was available from developing countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".