On the troubling use of plastic ‘habitat’ structures for fish in freshwater ecosystems – or – when restoration is just littering
Bibliographic record
Abstract
The creation and deployment of plastic structures made out of pipes and panels in freshwater ecosystems to enhance fish habitat or restore freshwater systems have become popularized in some regions. Here, we outline concerns with these activities, examine the associated evidence base for using plastic materials for restoration, and provide some suggestions for a path forward. The evidence base supporting the use of plastic structures in freshwater systems is limited in terms of ecological benefit and assurances that the use of plastics does not contribute to pollution via plastic degradation or leaching. Rarely was a cradle-to-grave approach (i.e. the full life cycle of restoration as well as the full suite of environmental consequences arising from plastic creation to disposal) considered nor were decommissioning plans required for deployment of plastic habitats. We suggest that there is a need to embrace natural materials when engaging in habitat restoration and provide more opportunities for relevant actors to have a voice regarding the types of materials used. It is clear that restoration of freshwater ecosystems is critically important, but those efforts need to be guided by science and not result in potential long-term harm. We conclude that based on the current evidence base, the use of plastic for habitat enhancement or restoration in freshwater systems is nothing short of littering.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".