Consumer Products using Triclosan as Antimicrobial Agent in the Tropical Environment: Safe or Unsafe?
Bibliographic record
Abstract
The tropical environments of the world receive the highest solar radiation throughout the year with an average annual temperature of not less than 18°C . The use of triclosan as an antimicrobial agent in consumer products such as toothpastes, mouthwash, soaps, under arm deodorants, liquid dishwashing soap and household equipment like textiles materials, toys and plastic kitchenware in this environment exponentially increased over the last few decades and its ability to form low chlorinated dioxins (polychlorinated dibenzodioxins and polychlorinated dibenzofurans) on intense solar radiation and incineration has posed a great risk potential to human health and the aquatic environments. This article aimed to create awareness to the general public on the potentially associated risks of using Triclosan containing materials and its possible implications on human health and the aquatic environments. Humans and aquatic animals get in contact with triclosan through oral, dermal, inhalation and accidental ocular exposures. In many temperate developed countries like US, UK, Canada and Australia, risk assessments were conducted on triclosan and its associated risks to Human health and aquatic environment, despites its traces in human body fluids and aquatic animal tissues, it is concluded that triclosan entering the environment in a way that poses no threat to humanity and aquatic lives. This conclusion came up with lots of uncertainties and conservatism especially due to a lack of adequate data to fully characterize the exposure to triclosan for the representation of the general population, dose estimation and conversion of spot urine samples for all age groups, adequacy of the margin of exposure (MOEs) and spatial differences between places. Moreover, in-depth research on triclosan and its potentialities to affect human health and the aquatic environment is strongly recommended especially in the developing tropical countries in order to create awareness to the general public.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".