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Are dams hotspots for <i>Didymosphenia geminata</i> blooms?

2009· article· en· W1992771243 on OpenAlexaffabout
Andrea E. Kirkwood, Leland J. Jackson, Edward McCauley

Bibliographic record

VenueFreshwater Biology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDiatomAbundance (ecology)EcologyBloomEnvironmental scienceAlgal bloomPlanktonBiologyPhytoplanktonNutrient

Abstract

fetched live from OpenAlex

Summary 1. The diatom Didymosphenia geminata has emerged in recent years as a globally invasive species. Although considered native to North America, reports of nuisance blooms have increased over the last decade. 2. Previously, we determined that D. geminata was ubiquitous in two major headwaters of the South Saskatchewan River Basin (SSRB), Alberta, Canada, but found it only bloomed at certain sites, including those immediately downstream from dam outfalls. To evaluate the role of dams in the abundance and blooming of D. geminata, we compared sites just below dams to unregulated upstream reference sites in six dammed rivers of the SSRB. 3. There was a high degree of seasonal variability in D. geminata abundance among sites, but statistical analyses showed a significant propensity for the diatom to have higher cell densities and an increased frequency of blooms at dam sites. 4. Important predictor variables of D. geminata abundance included dam presence, water clarity and total phosphorus concentration. When data from dam sites were analysed, a multiple regression model using mean discharge and pH as independent predictors explained 73% of the variation in D. geminata cell density. 5. Analysis of 3 years of data from one study river (Red Deer River) revealed consistently higher D. geminata cell densities at the dam site compared to the upstream reference. This analysis also showed that average cell density fluctuated by orders of magnitude from year‐to‐year. 6. Due to the ecological and aesthetic concerns regarding the global spread and blooming of D. geminata, we recommend that dams be viewed as key candidates for mitigating blooms in flow regulated systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.249
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations53
Published2009
Admission routes2
Has abstractyes

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