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Record W2161557051 · doi:10.2980/17-4-3364

Evaluation of microcrustacean (Cladocera, Chydoridae) biodiversity based on sweep net and surface sediment samples

2010· article· en· W2161557051 on OpenAlexvenueno aff
Liisa Nevalainen

Bibliographic record

VenueEcoscience · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
FundersWaldemar von Frenckells Stiftelse
KeywordsBiodiversitySpecies richnessCladoceraLittoral zoneEcologyEutrophicationSedimentEnvironmental scienceZooplanktonSpecies diversityBiologyNutrient

Abstract

fetched live from OpenAlex

Biodiversity (species richness and species diversity) of chydorid Cladocera were examined by weekly sweep net sampling during the ice-free period and through surface sediment assemblages in 9 limnologically different lakes in southern Finland. Species richness in the sweep net samples was low in 2 lakes with recent ecological perturbations and the highest in one oligotrophic lake. Species diversity was the lowest in 2 eutrophicated lakes and generally higher in the oligotrophic lakes. The biodiversity values in sediment assemblages showed trends similar to those in the sweep net samples; the lowest values were observed in one of the eutrophic lakes and the highest in the oligotrophic lakes. It is likely that the observed differences were due to the impact of nutrients on the development of littoral vegetation and consequently chydorid habitats and resources. The role of nutrients as a forcing mechanism on chydorid biodiversity was indicated by high and significant negative correlations between species diversity in both total phosphorus and sweep net and sediment samples. The comparison between biodiversity in the 2 sample types showed that the biodiversity values were almost consistently higher in the surface sediment assemblages than in the sweep net samples, suggesting that surface sediment analysis provides an effective alternative method to living material examination for biodiversity evaluations.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.000

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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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.

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

Citations26
Published2010
Admission routes1
Has abstractyes

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