Most harmful algal bloom species are vitamin B <sub>1</sub> and B <sub>12</sub> auxotrophs
Bibliographic record
Abstract
Eutrophication can play a central role in promoting harmful algal blooms (HABs), and therefore many HAB studies to date have focused on macronutrients (N, P, Si). Although a majority of algal species require exogenous B vitamins (i.e., auxotrophic for B vitamins), the possible importance of organic micronutrients such as B vitamins (B(1), B(7), B(12)) in regulating HABs has rarely been considered. Prior investigations of vitamins and algae have examined a relatively small number of dinoflagellates (n = 26) and a paucity of HAB species (n = 4). In the present study, the vitamin B(1), B(7), and B(12) requirements of 41 strains of 27 HAB species (19 dinoflagellates) were investigated. All but one species (two strains) of harmful algae surveyed required vitamin B(12), 20 of 27 species required B(1), and 10 of 27 species required B(7), all proportions higher than the previously reported for non-HAB species. Half-saturation (K(s)) constants of several HAB species for B(1) and B(12) were higher than those previously reported for other phytoplankton and similar to vitamin concentrations reported in estuaries. Cellular quotas for vitamins suggest that, in some cases, HAB demands for vitamins may exhaust standing stocks of vitamins in hours to days. The sum of these findings demonstrates the potentially significant ecological role of B-vitamins in regulating the dynamics of HABs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".