Corrigendum: Plant barcoding of a wildlife sanctuary across a wide climatic zone, Uttarakhand, India
Bibliographic record
Abstract
Background: Earlier, we successfully worked on two plant DNA barcode projects, one genera specific and the other one involving tree species from a province. In recent efforts, we are leading a consortium of six institutes of the Council of Scientific and Industrial Research to barcode plant species across India, where each institute is focusing on a respective phytogeographic location. As a part of this project, CSIR-NBRI is involved in DNA barcoding of a wild life sanctuary, Govind Wildlife Sanctuary, in the west Himalayan state of Uttarakhand. Results: We have collected 238 angiosperms, 270 bryophytes, 255 pteridophytes, 265 lichens, and 154 algae species from the sanctuary, spanning a wide climatic range, mainly due to a steep altitudinal gradient (1300–6315m.a.s.l.). So far we have analyzed 178 accessions of angiosperm plants from this hot spot using the four standard plant barcode loci. MatK was not used further after initial failure in PCR amplifications. PCR success ranged from 88% to 95%, trnH–psbA being the lowest and rbcL being the highest. ITS exhibited the lowest rate of sequencing success, while trnH–psbA exhibited the highest. ITS and trnH–psbA exhibited 90% success in species identification. Among the non-flowering plants, 75 accessions of lichen have been analyzed using ITS. PCR and sequencing success was 75% and 80%, respectively. Contamination with other fungi was a major problem faced during sequencing of lichen samples. Barcoding of other lower group of plant species from this region will be carried out in the near future. Under the consortium, our main aim is to develop a plant barcode data base of high-value medicinal plants. Significance: Barcoding the flora, including a lower group of plant species of a particular hot spot, spanning a wide range of climatic zones,will certainly impact on conservation, policymaking, and environmental protectionbydifferent government agencies. The study will also help test the efficacy in resolving species identification in a specific geographic region.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.032 |
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".