The biology and ecology of the invasive silver-cheeked toadfish (Lagocephalus sceleratus), with emphasis on the Eastern Mediterranean
Bibliographic record
Abstract
Invasive species pose threats to either human health or inflict ecological and/or economic damage. The silver-cheeked toadfish (Lagocephalus sceleratus), a Lessepsian species, is one of the most harmful species in the Mediterranean Sea, because of its potent neurotoxin, impacts on marine biodiversity, and the increased costs and labor they inflict on fishers. Since the catch and consumption of this pufferfish is prohibited by almost all countries bordering the Mediterranean, they have now expanded into the entire Mediterranean and Black Sea. We performed a comprehensive study of L. sceleratus covering ecological aspects, growth, reproduction, diet and trophic level based on samples from southwestern coasts of Turkey. The estimated growth parameters were L∞ = 88.7 cm, K = 0.27 year-1, C = 0.6 and WP = 0.1. Their sex-ratio was M/F = 1:0.69. Lagocephalus sceleratus appears to be a batch spawner with discontinuous oocyte recruitment and has different spawning seasons in the Eastern Mediterranean which seem to be based on temperature cues which get shorter in duration as one moves north from the Suez. We also report their first positive ecological trait, that they are controlling some other invasive species through their diets, such as lionfish, Red Sea goatfish, rabbitfish and longspine sea urchins, in addition to controlling themselves through cannibalism, which appears to be density-dependent. They are indeed a top predator in the region with a trophic level of 4.1. We suggest that targeted fishing using improved gear-types to reduce fishing gear damages are initiated, and that finding commercial markets for pufferfish could help to naturally fund ongoing control efforts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".