MétaCan
Menu
Back to cohort
Record W3036455104 · doi:10.1002/lol2.10164

Effects of lake warming on the seasonal risk of toxic cyanobacteria exposure

2020· article· en· W3036455104 on OpenAlexafffund
Nicole M. Hayes, Heather A. Haig, Gavin L. Simpson, Peter R. Leavitt

Bibliographic record

VenueLimnology and Oceanography Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaQueen's UniversityCanada Research ChairsQueen's University BelfastCanada Foundation for InnovationUniversity of Regina
KeywordsMicrocystinEnvironmental scienceSeasonalityCyanobacteriaClimate changeAlgal bloomEcologyNutrientGeographyBiologyPhytoplankton

Abstract

fetched live from OpenAlex

Abstract Incidence of elevated harmful algal blooms and concentrations of microcystin are increasing globally as a result of human‐mediated changes in land use and climate. However, few studies document changes in the seasonal and interannual concentrations of microcystin in lakes. Here, we modeled 11 yr of biweekly microcystin data from six lakes to characterize the seasonal patterns in microcystin concentration and to ascertain if there were pronounced changes in the patterns of potential human exposure to microcystin in lakes of central North America. Bayesian time series analysis with generalized additive models found evidence for a regional increase in microcystin maxima and duration but recorded high variation among lakes. During the past decade, warmer temperatures, but not nutrient levels, led to a marked increase in the number of days when concentrations exceeded drinking and recreational water thresholds set by the World Health Organization and United States Environmental Protection Agency.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.016
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

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

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 head0.004
GPT teacher head0.164
Teacher spread0.160 · 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 teacher head, 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

Citations48
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueLimnology and Oceanography LettersSame topicAquatic Ecosystems and Phytoplankton DynamicsFrench-language works237,207