Mitochondrial Cytochrome Oxidase I (mtCOI) Gene Sequence Based Identification of Subterranean Termite Species from the Punjab Province of Pakistan
Bibliographic record
Abstract
Correct species identification of subterranean termites is crucial for their effective control. However, achieving species-level identification has been difficult due to the complex biology, caste system and sexual dimorphism inherent to subterranean termites. In this study, worker and soldier castes of termites were collected from various locations across the Punjab province of Pakistan. Species identification was initially performed using the morphometric characteristics of the soldier caste. Subsequently, mitochondrial genes, including COI-5′ (barcode) and COI-3′ genes, were sequenced from both identified soldier and unidentified worker specimens. This molecular approach was employed to confirm species identities and establish phylogenetic relations. Both DNA barcodes and COI-3′ sequences successfully discriminated the analyzed termites to the species level. Cluster analysis showed species-level relationships between studied termite taxa. Disparity index test of species based on barcode data showed significant differences among most species pairs, highlighting the genetic diversity within the dataset. Nucleotide similarity analysis indicated that none of the species showed more than 98% similarity, with most of the species showing less than 89% similarity. Comparative analyses of COI-3′ sequences from the seven termite species, combined with the reference sequences available in the GenBank, consistently grouped the termite species of each genus into distinct, well-defined clusters. In conclusion, COI-5′ barcode and COI-3′ sequences provided robust nucleotide differentiation among the termite species, highlighting their effectiveness as precise molecular tools for species the discrimination and phylogenetic analysis of subterranean termites.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".