First DNA sequence reference library for mammals and plants of the Eastern Mediterranean Region
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".