Novel pink-spot disease in North American kelp nurseries
Bibliographic record
Abstract
Abstract Macroalgal cultivation is a growing industry in North America, but is well established in Asia and Africa, where macroalgal disease cause crop losses, placing a significant economic burden on farmers. As kelp cultivation intensifies in North America, kelp disease prevalence is expected to increase in tandem. With input from the kelp-growing community through an online survey, we describe the prevalence of a novel kelp disease, pink-spot disease, which has been observed in Canada and the United States on Saccharina latissima, Alaria marginata, Nereocystis luetkeana , and Macrocystis tenuifolia . Eight of the fourteen (57%) surveyed kelp growers report bright pink spots (pink-spot disease) on their kelp spools in the land-based nursery stage. Along with the grower survey, we conducted 16S rRNA amplicon sequencing in 2021 and 2022 to investigate the causative agent of pink-spot disease and associated bacterial community changes on infected Saccharina latissima (sugar kelp) spools. Our data in both years show that a member of the genus Algicola is enriched on visibly diseased spool regions compared to asymptomatic spool regions and may be the causative agent of pink-spot disease. As macroalgal cultivation continues to intensify, monitoring diseases is important to mitigate potential negative impacts.
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 | 0.000 | 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".