An On-Farm Workflow for Predictive Management of Paralytic Shellfish Toxin-Producing Harmful Algal Blooms for the Aquaculture Industry
Bibliographic record
Abstract
Paralytic shellfish toxins (PSTs) produced by marine dinoflagellates significantly impact shellfish industries worldwide. Early detection on-farm and with minimal training would allow additional time for management decisions to minimize economic losses. Here, we describe and test a standardized workflow based on the detection of sxtA4, an initial gene in the biosynthesis of PSTs. The workflow is simple and inexpensive and does not require a specialized laboratory. It consists of (1) water collection and filtration using a custom gravity sampler, (2) buffer selection for sample preservation and cell lysis for DNA, and (3) an assay based on a region of sxtA, DinoDtec lyophilized quantitative polymerase chain reaction (qPCR) assay. Water samples spiked with Alexandrium catenella showed a cell recovery of >90% when compared to light microscopy counts. The performance of the lysis method (90.3% efficient), Longmire’s buffer, and the DinoDtec qPCR assay (tested across a range of Alexandrium species (90.7–106.9% efficiency; r 2 > 0.99)) was found to be specific, sensitive, and efficient. We tested the application of this workflow weekly from May 2016 to 30th October 2017 to compare the relationship between sxtA4 copies L –1 in seawater and PSTs in mussel tissue ( Mytilus galloprovincialis ) on-farm and spatially (across multiple sites), effectively demonstrating an ∼2 week early warning of two A. catenella HABs ( r = 0.95). Our tool provides an early, accurate, and efficient method for the identification of PST risk in shellfish aquaculture.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".