Reach- and mat-scale differences in <i>Microcoleus autumnalis</i> (cyanobacterium) accrual along velocity and nitrate gradients in three New Zealand rivers
Bibliographic record
Abstract
Proliferations of the toxic, mat-forming cyanobacterium Microcoleus autumnalis are an increasingly recognized problem in cobble-bed rivers worldwide. This study explored how flow and nutrient concentrations influence mat expansion. Microcoleus autumnalis was inoculated into cobbles placed in runs, riffles, and pools in three rivers with different nutrient conditions, and mat size was monitored over 21 days. The following hypotheses were tested: (i) mat expansion will reflect cover increases at the reach scale; (ii) biomass and cover will be highest in high-velocity habitats; and (iii) under similar velocities, nutrient concentrations will be more important than other abiotic and biotic variables in determining expansion rates. Mat expansion accurately reflected the increase in reach-scale cover, and expansion was most rapid at intermediate water velocities (0.25–0.45 m·s–1). Mats persisted the longest in riffles. Accrual cycles were terminated earlier in runs than in riffles, as high expansion rates resulted in patches reaching maximum mat size rapidly. Although M. autumnalis accrual differed among rivers, this was attributed to differences in shear stress and grazing pressure rather than nutrient concentrations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".