Environmental DNA captures the genetic diversity of bowhead whales (Balaena mysticetus) in West Greenland
Bibliographic record
Abstract
Photo caption and photo credit Caption (Upper left): Researchers studying the genetic diversity of bowhead whales (Balaena mysticetus) in Disko Bay, West Greenland, by use of environmental DNA from seawater samples Caption (Upper middle): eDNA water sampling in the Rupert river, James Bay, Québec, Canada. From Englobe Corp.: Damien Boivin-Delisle (left), Jean-Denis Simard Caption (Upper right): The Melach, a glacier-fed, fish-free river in Tyrol (Austria, 47°06’59.9”N 11°08’04.5”E) was used to examine lateral and longitudinal fish eDNA distribution downstream of the source. Fish were placed in cages (visible half-submerged in the stream in center of the picture) and water samples for eDNA analysis were taken during summer, fall and winter. This picture shows the situation in summer during the early afternoon, which is characterized by high discharge and high turbidity. Caption (Middle left): Sampling eDNA sediment samples at Bonanza Springs Caption (Middle): Researchers from University of Duisburg-Essen screen environmental DNA extracted from stream samples for traces of benthic invertebrates with improved lab protocols Caption (Middle): A greater mouse-eared bat Myotis myotisentering an underground cavity during the swarming period, Saint-Savinien, Charente-Maritime, France Caption (Middle right): Mule deer drinking water at Ahn Spring in the Mojave Desert Caption (Lower left): Researchers from University Duisburg-Essen, Germany, analyse macroinvertebrate environmental DNA obtained from the Long-Term Ecological Research site Rhine-Main-Obersatory (drone view) Credit: Till-Hendrik Macher (lab picture); Robert Marc Lehmann www.robertmarclehmann.com (drone view) Caption (Lower middle): Redfish (Sebastes sp.) deep trawler catch on the Téleost research vessel Caption (Lower right): Mountain lions drinking water at Ahn Spring in the Mojave Desert
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".