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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".