Spatiotemporal transitions in Pseudo-nitzschia species assemblages and domoic acid along the Alaska coast
Bibliographic record
Abstract
The toxic diatom genus Pseudo-nitzschia is distributed from equatorial to polar regions and is comprised of >57 species, some capable of producing the neurotoxin domoic acid (DA). In the Pacific Arctic Region spanning the Bering, Chukchi, and Beaufort seas, DA is recognized as an emerging human and ecosystem health threat, yet little is known about the composition and distribution of Pseudo-nitzschia species in these waters. This investigation characterized Pseudo-nitzschia assemblages in samples collected in 2018 during summer (August) and fall (October-November) surveys as part of the Distributed Biological Observatory and Arctic Observing Network, encompassing a broad geographic range (57.8° to 73.0°N, -138.9° to -169.9°W) and spanning temperature (-1.79 to 11.7°C) and salinity (22.9 to 32.9) gradients associated with distinct water masses. Species were identified using a genus-specific Automated Ribosomal Intergenic Spacer Analysis (ARISA). Seventeen amplicons were observed; seven corresponded to temperate, sub-polar, or polar Pseudo-nitzschia species based on parallel sequencing efforts (P. arctica, P. delicatissima, P. granii, P. obtusa, P. pungens, and two genotypes of P. seriata), and one represented Fragilariopsis oceanica. During summer, particulate DA (pDA; 4.0 to 130.0 ng L-1) was observed in the Bering Strait and Chukchi Sea where P. obtusa was prevalent. In fall, pDA (3.3 to 111.8 ng L-1) occurred along the Beaufort Sea shelf coincident with one P. seriata genotype, and south of the Bering Strait in association with the other P. seriata genotype. Taxa were correlated with latitude, longitude, temperature, salinity, pDA, and/or chlorophyll a, and each had a distinct distribution pattern. The observation of DA in association with different species, seasons, geographic regions, and water masses underscores the significant risk of Amnesic Shellfish Poisoning (ASP) and DA-poisoning in Alaska waters.
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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 | 0.001 |
| 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) | 0.001 | 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".