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Record W2549235783 · doi:10.2298/abs151229072b

Developmental instability in German Iris flower as a potential biomonitoring method

2016· article· en· W2549235783 on OpenAlexaff
Nataša Barišić Klisarić, Danijela Miljković, Stevan Avramov, Uroš Živković, Aleksej Tarasjev

Bibliographic record

VenueArchives of Biological Sciences · 2016
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsInstitute for Biological Sciences
FundersMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsFluctuating asymmetryBiomonitoringUnivariateBiologyPollutionPerianthIRIS (biosensor)Multivariate statisticsEnvironmental scienceEcologyHorticultureMathematicsStatistics

Abstract

fetched live from OpenAlex

In light of the increasing need for appropriate, cost-effective detection methods of anthropogenic pollution, we evaluated the biomonitoring potential of flower developmental instability (DI) on a widely planted decorative species, Iris germanica, under in situ conditions. DI was measured by fluctuating and radial asymmetries of parts of Iris germanica perianth (810 fall lengths and widths), from clones already growing in two distinct types of habitats with contrasting levels of anthropogenic pollution: in unpolluted (rural) areas, Novi Banovci, Stari Banovci and Belegis (flowers from 137 clones sampled), and in a polluted (urban) Belgrade metropolitan area (flowers from 133 clones sampled). Our results revealed significantly higher flower radial asymmetry in the polluted habitats compared to unpolluted ones (for three out of four univariate indices, as well as both multivariate ones), but failed to detect a similar effect on fluctuating asymmetry indices. The results of our study therefore demonstrate the potential of DI (when estimated by flower radial asymmetry) in Iris germanica as a cost-effective biomonitoring method for in situ pollution detection based on readily measurable flower parts and moderate sample sizes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.078
GPT teacher head0.375
Teacher spread0.298 · 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 designBench or experimental
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

Citations3
Published2016
Admission routes1
Has abstractyes

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