DNA Barcode Authentication of Wood Samples of Threatened and Commercial Timber Trees within the Tropical Dry Evergreen Forest of India
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: India is rich with biodiversity, which includes a large number of endemic, rare and threatened plant species. Previous studies have used DNA barcoding to inventory species for applications in biodiversity monitoring, conservation impact assessment, monitoring of illegal trading, authentication of traded medicinal plants etc. This is the first tropical dry evergreen forest (TDEF) barcode study in the World and the first attempt to assemble a reference barcode library for the trees of India as part of a larger project initiated by this research group. METHODOLOGY/PRINCIPAL FINDINGS: We sampled 429 trees representing 143 tropical dry evergreen forest (TDEF) species, which included 16 threatened species. DNA barcoding was completed using rbcL and matK markers. The tiered approach (1st tier rbcL; 2nd tier matK) correctly identified 136 out of 143 species (95%). This high level of species resolution was largely due to the fact that the tree species were taxonomically diverse in the TDEF. Ability to resolve taxonomically diverse tree species of TDEF was comparable among the best match method, the phylogenetic method, and the characteristic attribute organization system method. CONCLUSIONS: We demonstrated the utility of the TDEF reference barcode library to authenticate wood samples from timber operations in the TDEF. This pilot research study will enable more comprehensive surveys of the illegal timber trade of threatened species in the TDEF. This TDEF reference barcode library also contains trees that have medicinal properties, which could be used to monitor unsustainable and indiscriminate collection of plants from the wild for their medicinal value.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it