Forensic botany and forensic chemistry working together: application of plant DNA barcoding as a complement to forensic chemistry—a case study in Brazil
Bibliographic record
Abstract
Recently, Brazilian Federal Police used forensic chemistry and forensic botany techniques on a case. Two packets containing fragmented plant matter were seized and sent for forensic analysis. Forensic chemistry, the gold standard for evaluating plant material suspected to contain illicit substances, did not find illicit materials. Gas chromatography coupled mass spectrometry (GC-MS) identified thujone in the botanical material. Thujone is a chemical compound naturally found in many plant species, notably Artemisia absinthium. Because doubt remained, we next used plant DNA barcoding methods. Total DNA from plant tissue fragments was extracted and five different DNA regions were amplified, sequenced, and analyzed using plant DNA barcoding methods. Genetic analysis yielded 30 good quality sequences representing five taxa. Most specimens were identified as A. absinthium. Few studies focus on practical forensic applications of plant DNA barcoding methods using a case solved in a forensic laboratory with its difficulties and limitations. To the best of our knowledge, this is the first study to report an effective joint effort of forensic chemistry and botany techniques to assess plant material in Brazil. The availability of a new technical approach for the genetic sequencing of plant species will enhance many forensic investigations and inspire similar initiatives.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".