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Record W2120732389 · doi:10.6000/1927-5129.2014.10.41

Detecting Intraspecific Character Displacement by Morphological Markers in Riverine-Dwelling Invertebrate Larvae: The Case Study of Head Shape Variability in Leuctra fusca (Plecoptera: Leuctridae)

2014· article· en· W2120732389 on OpenAlexvenueno aff
Raffaella Bravi, Lorenzo Traversetti, Massimiliano Scalici

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2014
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsnot available
Fundersnot available
KeywordsMorphometricsSuperimpositionPrincipal component analysisBiologyIntraspecific competitionProcrustes analysisMeristicsZoologyInvertebrateEcologyStatisticsMathematicsGeometry

Abstract

fetched live from OpenAlex

Since morphological markers are recognized as useful tools to evaluate events of anthropic disturbances, we performed a preliminary study on head shape variability in the riverine-dwelling Leuctra fusca larvae as early alarm systems in running waters. Particularly, heads of 32 larvae were collected from two localities of River Aniene (central Italy) and photographed for digitizing landmarks and semilandmarks. The Cartesian x-y coordinates of all points were firstly converted to shape coordinates by Procrustes superimposition, and then analyzed for exploring the full potential of the application of geometric morphometric techniques. Where the principal component analysis revealed a clear pattern of variation between the 2 sampling sites, the Procrustes ANOVA highlighted this variation as highly associated with fluctuating asymmetry, the latter being traditionally connected with developmental accidents due to environmental conditions. No directional asymmetry was observed. Finally we didn’t find any pattern of allometric variation in the studied structure. Our study indicates that further studies ought to be employed to use geometric morphometrics as a valid tool for detecting and describing morphological variation as biomarkers in invertebrate organism such as stoneflies.

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)0.011
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.292
Teacher spread0.248 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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