DNA barcoding in surveys of small mammal communities: a field study in Suriname
Bibliographic record
Abstract
The performance of DNA barcoding as a tool for fast taxonomic verification in ecological assessment projects of small mammals was evaluated during a collecting trip to a lowland tropical rainforest site in Suriname. We also compared the performance of tissue sampling onto FTA CloneSaver cards vs. liquid nitrogen preservation. DNA barcodes from CloneSaver cards were recovered from 85% of specimens, but DNA degradation was apparent, because only 36% of sequence reads were long (over 600 bp). In contrast, cryopreserved tissue delivered 99% barcode recovery (97% > 600 bp). High humidity, oversampling or tissue type may explain the poor performance of CloneSaver cards. Comparison of taxonomic assignments made in the field and from barcode results revealed inconsistencies in just 3.4% of cases and most of the discrepancies were due to field misidentifications (3%) rather than sampling/analytical error (0.5%). This result reinforces the utility of DNA barcoding as a tool for verification of taxonomic identifications in ecological surveys, which is especially important when the collection of voucher specimens is not possible.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".