Coastal upwelling linked to toxic Pseudo-nitzschia australis blooms in Los Angeles coastal waters, 2005–2007
Bibliographic record
Abstract
Harmful algal blooms dominated by the diatom Pseudo-nitzschia spp. have become a perennial but variable event within surface waters near the greater Los Angeles area. Toxic blooms during spring seasons from 2005 to 2007 varied strongly in their overall toxicity and duration. Differences in bloom dynamics were linked to differences in storm-induced river discharge following episodic rain events and coastal upwelling, both major coastal processes that led to the injection of nutrients into coastal surface waters. Heavy river runoff during early 2005, a record-rainfall year, favored a phytoplankton community mainly comprised of algal taxa other than Pseudo-nitzschia. The spring bloom during 2005 was associated with low domoic acid surface concentrations and minor contributions of (mainly) P. delicatissima to the diatom assemblage. In contrast, highly toxic P. australis-dominated blooms during spring seasons of 2006 and 2007 were linked to strong upwelling events. River discharge quotas in 2006 and 2007, in contrast to 2005, fell well below annual averages for the region. Surface toxin levels were linked to colder, more saline (i.e. upwelled) water over the 3-year study, but no such consistent relationship between domoic acid levels and other physiochemical parameters, such as macronutrient concentrations or nutrient ratios, was observed.
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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".