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Record W3090000033 · doi:10.1139/gen-2019-0194

First DNA sequence reference library for mammals and plants of the Eastern Mediterranean Region

2020· article· en· W3090000033 on OpenAlexvenueno aff
Liliane Boukhdoud, Carole Saliba, Lillian D. Parker, Nancy Rotzel McInerney, Ghiwa Ishak Mouawad, Mariane Kharrat, Rhea Kahale, Tony Chahine, Jesús E. Maldonado, Magda Bou Dagher Kharrat

Bibliographic record

VenueGenome · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsnot available
FundersSaint Joseph UniversityEli Lilly and Company
KeywordsBiologyBiodiversityEndangered speciesThreatened speciesSpecies richnessDNA barcodingFaunaCritically endangeredEcologyBiodiversity hotspotMammalMediterranean climateEndemismHabitat

Abstract

fetched live from OpenAlex

The Mediterranean region is identified as one of the world’s 36 biodiversity hotspots, with the Earth’s most biologically rich yet threatened areas. Lebanon is a hub for Eastern Mediterranean Region (EMR) biodiversity with 9116 characterized plant and animal species (4486 fauna and 4630 flora). Using DNA barcoding as a tool has become crucial in the accurate identification of species in multiple contexts. It can also complement species morphological descriptions, which will add to our understanding of the biodiversity and richness of ecosystems and benefit conservation projects for endangered and endemic species. In this study, we create the first reference library of standard DNA markers for mammals and plants in the EMR, with a focus on endemic and endangered species. Plant leaves were collected from different nature reserves in Mount Lebanon, and mammal samples were obtained from taxidermized museum specimens or road kills. We generated the 12S rRNA sequences of 18 mammal species from 6 orders and 13 different families. We also obtained the trnL and rbcL barcode sequences of 52 plant species from 24 different families. Twenty-five plant species and two mammal species included in this study were sequenced for the first time using these markers.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.170

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 head0.093
GPT teacher head0.265
Teacher spread0.172 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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