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Record W2233843525 · doi:10.5376/amb.2015.05.0001

Molecular characterization of giant African land snails using polymerase chain reaction - random amplified polymorphic DNA fingerprinting

2015· article· en· W2233843525 on OpenAlexvenueno aff
Mitsheal Odey, Peter O. Aikpokpodion, Ebiamadon Andi Brisibe

Bibliographic record

VenueAnimal Molecular Breeding · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMollusks and Parasites Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPolymerase chain reactionDNA profilingCharacterization (materials science)DNAMultiplex polymerase chain reactionBiologyChemistryMolecular biologyGeneticsGeneMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

Random amplified polymorphic DNA (RAPD) markers were used to characterize the genetic diversity and relatedness among different populations of Archachatina marginata, a highly relished source of protein in West Africa. Sixteen (16) accessions comprising nine (9) black and seven (7) albino-bodied forms were collected randomly from three different locations in Nigeria and genetic differentiation and morphometric studies conducted. In the genetic analysis, a total of 84 reproducible bands were produced using three (3) oligonucleotide primers. Of these, 79 amplified bands (94.04%) were found to be polymorphic with an average of 28 bands per primer while the remaining 5 were monomorphic loci. Similarly, an analysis of morphological traits resulted in the division of the entire population into 2 major groups based on the geographical distribution that generally reflected expected trends between the genotypes. Among all the morphometric characters, the highest mean value was observed in shell spire (23.38) while the lowest mean value was observed in stripes on the blossom end (1.31). In conclusion, average linkage cluster analysis revealed a high level of genetic diversity and heterogeneity among the snail accessions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.429

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 head0.043
GPT teacher head0.241
Teacher spread0.198 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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