Examination of two new technologies to assess the diet of woodland caribou: video recorders attached to collars and DNA barcoding
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
The diet of woodland caribou (Rangifer tarandus caribou Gmelin, 1788) in the boreal zone of North America is poorly understood. In large part this is because they occur at low densities in environments that are difficult to access. The only method available for identifying food requirements of wildlife has been histological examination of fecal samples, a technique that suffers from a number of serious limitations. Our study used fecal samples from 125 woodland caribou and animal-borne videos to address two questions: (1) How do the new technologies, video cameras and DNA barcoding, compare with conventional diet analyses of fecal pellets? and (2) Can these techniques be used to determine the diet of woodland caribou? Our results show that microhistology estimates provide an inaccurate approximation of diet; <15% correlation with either barcoding or video techniques. Taxonomic resolution of the histology estimates was very low. Taxonomic resolution to species found in fecal samples was good using video (42%) but better using DNA barcoding (94%). DNA barcoding and video technologies provided dietary data that were highly correlated (70%) among major groups of plants, with terrestrial lichens dominating the late winter through early spring diet. The high species resolution of DNA barcoding may be supplemented by information on habitat preference and degree of feeding selectivity obtained by video surveillance. These novel technologies may be important tools for identifying critical habitat requirements and associated conservation strategies needed for elusive wildlife species that are endangered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".