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Record W2010304014 · doi:10.2298/abs0503237j

Observation of the quality of Danube water in the Belgrade region based on benthic animals during periods of high and low water conditions in 2002

2005· article· en· W2010304014 on OpenAlexaff
Dunja Jakovčev-Todorović, Momír Paunović, Bojana Stojanovic, Vladica Simić, Vesna Djikanović, Ana Veljkovic

Bibliographic record

VenueArchives of Biological Sciences · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInvertebrate Taxonomy and Ecology
Canadian institutionsInstitute for Biological Sciences
Fundersnot available
KeywordsBenthic zoneBenthosSpecies richnessWater qualityAbundance (ecology)Environmental scienceCommunity structureEcologyPollutionInvertebratePollutantGeographyBiotic indexSampling (signal processing)Biology

Abstract

fetched live from OpenAlex

The present paper states conclusions about the quality of Danube water in the Belgrade Region based on analyses of the invertebrate community. The investigation was performed during periods of high (May, 2002) and low (October, 2002) water conditions. Meio- and macrozoobenthos were observed. Qualitative, quantitative, and saprobiological analy?ses were performed. The sampling area covered five stations along 66 km of the river. The community was represented by 26 species. Aquatic worms were the principal component of the benthos with respect to both species richness (six species) and abundance (58.39-99.47 % of the total community). Gastropods were also diverse (six species). Snails were found to be subdominant as far as participation in the total community density is concerned. Structure of the benthic community and the saprobity index (S= 2.78-3.43) indicated the presence of organic pollution. No notable differences of estimated environmental quality were observed between a station upstream from Belgrade and one situated below the exit from the broader territory of Belgrade. Since Belgrade is recognized as one of the main contaminants in regard to biodegradable pollutants in the Middle Danube, this finding points to an impressive self-purifying ability of this huge river.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.064
GPT teacher head0.251
Teacher spread0.187 · 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

Citations6
Published2005
Admission routes1
Has abstractyes

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