Declining phosphorus as a potential driver for the onset of <i>Didymosphenia geminata</i> mats in <scp>N</scp>orth <scp>A</scp>merican rivers
Bibliographic record
Abstract
Abstract Didymosphenia geminata is a stalk‐forming diatom capable of creating thick benthic mats in low‐nutrient streams. There are two hypotheses to explain the rapid worldwide increase in occurrence of nuisance D. geminata mats: (a) Cells are spread among rivers and across broad ecoregions through natural and anthropogenic vectors, or (2) pre‐existing D. geminata populations are forming mats in response to changing environmental conditions within the habitat. Low phosphorus (P) concentrations are a major trigger for stalk production by D. geminata cells. Although the environmental change hypothesis is gaining support among researchers, long‐term data sets demonstrating declining P concentrations prior to reported mat formation have been essentially absent from the literature. Here, we present long‐term datasets for two case studies for which long‐term P and D. geminata data coincide: the Matapedia River in Eastern Canada and the Kootenai River below Libby Dam in Montana, United States. Both rivers had declining P levels over time. However, there was a 2‐ and 20‐year lag time, respectively, between mat development and reaching the previously proposed average 2 μg/L soluble reactive P threshold for development. Although the Matapedia River provides some support of the environmental change hypothesis, the Kootenai River data set suggests other environmental factors may play a role in mat development. The data presented do not rule out the environmental change hypothesis but do suggest there may be conditions in addition to low P that must be met for mats to form and the environmental change hypothesis can likely be refined to include more parameters to better understand and mitigate the influence of mats.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".