The environmental behaviour of polychlorinated phenols and its relevance to cork forest ecosystems: a review
Bibliographic record
Abstract
Pentachlorophenol (PCP) has been used as a herbicide, biocide and preservative worldwide since the 1930s and as a result, extensive and prolonged contamination exists. The environmental impact increases when its many degradation products are taken into consideration. A number of chloroanisols and their related chlorophenols have been found in cork slabs collected from Portuguese oak tree forests before stopper manufacturing, and contamination by PCP and polychlorinated anisole (PCA) has been detected in Canadian forests. It is suggested that the use of polychlorinated phenols, in particular PCP, is thought to be a cause of the cork taint problem in wine, a major socio-economic impact not only for industry but on sensitive and highly biodiverse ecosystems. It also highlights particular issues relating to the regional regulation of potentially toxic chemicals and global economics world wide. To fully understand the impact of contamination sources, the mechanisms responsible for the fate and transport of PCP and its degradation products and assessment of their environmental behaviour is required. This review looks at the current state of knowledge of soil sorption, fate and bioavailability and identifies the challenges of degradation product identification and the contradictory evidence from field and laboratory observations. The need for a systematic evaluation of PCP contamination in relation to cork forest ecosystems and transfer of PCP between trophic levels is emphasised by discrepancies in bioaccumulation and toxicity. This is essential to enable long term management of not only transboundary contaminants, but also the sustainable management of socially and economically important forest ecosystems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".