Expansion of aquaculture parks and the increasing risk of non‐native species invasions in Brazil
Bibliographic record
Abstract
Abstract Aquaculture is the main vector for introduction of non‐native species in Brazil and around the world. Despite the potentially serious and irreversible ecological impacts caused by non‐native species, they continue to be in many cases the preferred option in aquaculture farms, of which the recent plans of aquaculture expansion promoted by the Brazilian Government are an emblematic example. In this study, we present a survey of publicly available information on aquaculture parks to be installed across the Brazilian territory, with emphasis on species status as native or non‐native, and discuss the implications for the conservation of aquatic biodiversity. One hundred and thirty‐nine aquaculture parks (APs), with a total of 1556 sites covering 941.38 hectares, have been called for bids. Among these, 122 APs will contain at least one non‐native species, and 68 APs will be based exclusively on their cultivation. A predictable consequence is the enhancement of propagule pressure in surrounding aquatic ecosystems, increasing the risk of non‐native species establishment or persistence, which will likely intensify the environmental impacts already in course in four major river basins and along the Brazilian coast. These impacts will add up to more direct effects of aquaculture farming – for example elevated input of nutrients and organic matter – and include changes in habitat and water quality, spread of diseases, biotic homogenization, loss of population viability resulting from hybridization and outbreeding depression, and the local extirpation of native species.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".