An evaluation of electrofishing as a control measure for an invasive tilapia (Oreochromis mossambicus) population in northern Australia
Bibliographic record
Abstract
Combating the spread of invasive fish is problematic, with eradication rarely possible and control options varying enormously in their effectiveness. In two small impoundments in north-eastern Australia, an electrofishing removal program was conducted to control an invasive tilapia population. We hypothesised that electrofishing would reduce the population density of Oreochromis mossambicus (Mozambique tilapia), to limit the risk of downstream spread into areas of high conservation value. We sampled by electrofishing monthly for 33 months. Over this period, there was an 87% decline in catch per unit effort (CPUE) of mature fish, coupled with a corresponding increase of 366% in the number of juveniles, suggesting a density-dependent response in the stock–recruitment relationship for the population. Temperature was inversely related to CPUE (r = 0.43, lag = 10 days), implying greater electrofishing efficiency in cooler months. The reduction in breeding stock is likely to reduce the risk of spread and render the population vulnerable to other control measures such as netting and/or biological control. Importantly, the current study suggests routine electrofishing may be a useful control tool for invasive fish in small impoundments when the use of more destructive techniques, such as piscicides, is untenable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".