Pentachlorophenol and its effect on different environmental matrices: the need for an alternative wood preservative
Bibliographic record
Abstract
Abstract Wood is considered to be one of the most important materials in the global economy. Wood loses shelf life in countries with severe winters. Pentachlorophenol (PCP) was predominantly used as a wood preservative because of its efficacy in controlling wood decay. This study discussed the overall impact of PCP across various environmental matrices, including soil, plants, water, air, and humans. Pentachlorophenol is a phenol substituted with five chloro-groups. High chlorination levels and stability of PCP make it hazardous to the environment, and persistent, and it also interferes with human, aquatic, and soil microbial health. PCP is volatile; thus, it is constantly discharged into the atmosphere and ingested by the human population. Owing to its hazardous nature, it was added to the Stockholm Convention’s list of persistent organic pollutants (POPs) and phased out of the industry in 2023. This review has summarized PCP properties, usage, production volume, different transformation pathways, and its harmful effects on different environmental matrices such as air, water, soil, crops, and human health which have helped to outline the expected features of the new chemical compared with PCP. The improved chemical is intended to have quick degradability, fewer chlorine atoms, no aromatic structure, be non-toxic, environmentally benign, and efficient against wood deterioration while also penetrating the wood better.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".