Evaluation of light traps for sampling lobster larvae in the German Bight, North Sea
Bibliographic record
Abstract
Biological monitoring of planktonic animals is greatly dependent on the deployment of traps. A variety of specialized traps have been designed for surface plankton and vertebrates. However, certain groups, such as planktonic larvae of benthic marine invertebrates remain underrepresented in sampling efforts. Catching them has proven to be more challenging because of their size, swimming ability, location, and abundance. In the present study a successful light trap for sampling American lobster larvae in New Brunswick, Canada, is evaluated on the island of Helgoland (German Bight, North Sea). Our results showed the traps were successful in catching larvae in laboratory experiments but were unable to catch European lobster larvae in the field. Traps deployed in the field were successful in capturing other benthic and pelagic zooplankton predominantly consisting of crustaceans from the orders: Cumacea, Amphipoda, Mysida and Isopoda. The low density of lobster larvae, the island's topography, and their unique photactic response possibly limited the success rate of the light traps. Future research is needed to construct a specialized trap to sample Helgoland's lobster larvae and provide information on the current larval fitness and population numbers.
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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) | 0.000 | 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) | 0.000 | 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".