The diatom <i>Lindavia intermedia</i> identified as the producer of nuisance pelagic mucilage in lakes
Bibliographic record
Abstract
ABSTRACT Populations of a centric diatom that produces copious extracellular polymeric substance (EPS), known as ‘lake snow’, have developed in several large microtrophic lakes in New Zealand over the past 10 years. The EPS coats fishing lines and blocks water filters. The phenomenon was first noticed in Lake Wanaka in the early 2000s and has recently been reported in Lakes Coleridge and Wakatipu, with single, isolated historical events occurring in Lakes Waikaremoana (confirmed) and Benmore (presumed). The species has been reported from a handful of other lakes in New Zealand, all except one of which are microtrophic‐to‐oligotrophic. Light and ultrastructural microscopic studies of New Zealand populations, DNA sequencing and comparison with published descriptions identify the causative species as Lindavia intermedia , part of the ‘bodanicoid’ complex. These species are best known from the Northern Hemisphere where they are regarded as confined to nutrient‐poor habitats, frequently having disappeared from European lakes as the lakes underwent eutrophication. Lake snow is known from a small number of other lakes in the Northern Hemisphere, but no evidence has been reported linking L. intermedia to the production of lake snow in these lakes. The expected growth characteristics (slow and at depth) of L. intermedia pose difficulties for any prospective containment campaign.
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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 | 0.001 | 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) | 0.001 | 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".