Microcystins in European Noble Crayfish Astacus astacus in Lake Steinsfjorden, a Planktothrix-Dominated Lake
Bibliographic record
Abstract
Lake Steinsfjorden, an important Norwegian location for noble crayfish (Astacus astacus), is often affected by cyanobacterial blooms caused by microcystin (MC)-producing Planktothrix spp. The impact of MCs on noble crayfish as a food source and crayfish health is largely unknown. We investigated the quantities and correlations of MCs in noble crayfish and lake water during and after a cyanobacterial bloom peaking in June–July 2015. Noble crayfish and water samples were collected monthly from June to October 2015 and in October 2016. The content of MCs was analysed by ELISA from tail muscle, intestine, stomach and hepatopancreas. PCR analysis for Planktothrix gene markers was performed on crayfish stomach content. Water samples were analysed for phytoplankton composition, biomass and MCs. PCR-positive stomach contents indicated Planktothrix to be part of the noble crayfish diet. Concentrations of MCs were highest in the hepatopancreas, stomach and intestine, peaking in August–September. Tail muscle contained low concentrations of MCs. Similar levels of MCs were found in crayfish from 2016. Except in September 2015, a normal portion of boiled noble crayfish tails was below the tolerable daily intake (TDI) for MCs for humans. Removing the intestine more than halved the content of MCs and seems a reasonable precautionary measure for noble crayfish consumers.
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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 | 0.001 | 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".