Soft plastic fishing lures as a potential source of chemical pollution - Chemical analyses, toxicological relevance, and anglers’ perspectives
Bibliographic record
Abstract
Soft plastic lures (SPLs) are commonly used artificial lures in recreational angling. Anglers regularly lose SPLs while fishing and there is little knowledge about the environmental impacts of lost SPLs. As with other plastic items, SPLs contain phthalates and other persistent additives that may leach into water. In this study, 16 randomly chosen SPLs of common models were analyzed for the leaching of persistent, water-soluble plastic additives, including phthalates. The estrogenicity of sample extracts from a subsample of 10 SPLs was assessed using luciferase reporter gene bioassays. Over a period of 61 days, 10 of the 16 SPLs leached the targeted phthalates dimethyl phthalate (DMP), diethyl phthalate (DEP), benzyl butyl phthalate (BBP) and di-n-butyl phthalate (DnBP) at median detectable concentrations ranging from 10 ng/g sample BBP to a median of 1001 ng/g DMP as well as 45 persistent, mobile, and toxic (PMT) plastic additives. DEP was detected most frequently in 8 SPLs, followed by BBP (2 SPLs), DMP (2 SPLs) and DnBP (1 SPL). The extract from one SPL with comparatively low phthalate and PMT plastic additive levels was active in the bioassay, indicating high endocrine-disruptive potential, presumably due to unknown additives that were not among the target substances of the methodology used in this study. The study was supplemented by a mail survey among anglers, in which attitudes of anglers towards SPLs were investigated. The survey indicated that SPL loss is a common event during angling. Most participants were concerned about potential ecological impacts of SPLs, wanted the ingredients of SPLs to be labelled and supported legal restrictions concerning toxic ingredients of SPLs. The study shows that SPLs are a potential source of environmental pollution, may pose human health risks and need further investigation, considering the frequent use of SPLs in recreational angling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".