Microcystin profiles in European noble crayfish Astacus astacus and water in Lake Steinsfjorden, Norway
Bibliographic record
Abstract
Lake Steinsfjorden, an important noble crayfish (Astacus astacus) habitat, is often affected by blooms of Planktothrix spp. that produce microcystins (MCs). A poor correlation between MCs by ELISA in the water and in crayfish tissue in a study in 2015 prompted further investigation by LC–HRMS. LC–HRMS analyses of filters from water samples and on selected crayfish tissue extracts from the 2015 study revealed the presence of known and previously unreported MCs. Crayfish samples from May and June 2015 were dominated by MCs from the Planktothrix bloom, whereas in September novel MCs that appeared to be metabolites of MC-LR were dominant, even though neither these nor MC-LR were detected in the water in 2015. A water sample from October 2016 also showed MCs typical of Planktothrix (i.e., [D-Asp3]- and [D-Asp3,Dhb7]MC-RR and -LR), but low levels of MC-RR and MC-LR were detected in the lake water for the first time. In late summer and autumn, the MC profiles of crayfish were dominated by the homonorvaline (Hnv) variant MC-LHnv, a putative metabolite of MC-LR. Taken together, ELISA, LC–HRMS and previous PCR analyses showed that although Planktothrix was part of the crayfish diet, it was not the sole source of MCs in the crayfish. Possibly, crayfish in Lake Steinsfjorden may be ingesting MCs from benthic cyanobacteria or from contaminated prey. Therefore, information on the cyanobacterial or MC content in the water column cannot safely be used to make predictions about MC concentrations in the crayfish in Lake Steinsfjorden. Interestingly, the results also show that targeted LC–MS analysis of the crayfish would at times have underestimated their MC content by nearly an order of magnitude, even if all previously reported MC variants had been included in the analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".