First records of two freshwater mussel Species at Risk, Mapleleaf (<i>Quadrula quadrula</i>) and Lilliput (<i>Toxolasma parvum</i>), in the Canard River, Ontario, with implications for freshwater mussel recovery in the Detroit River
Bibliographic record
Abstract
Freshwater mussels of the family Unionidae are among the world’s most imperilled animals. A third of Canadian species have been assessed by the Committee on the Status of Endangered Wildlife in Canada as Extirpated, Endangered, Threatened, or Special Concern, with losses attributed to natural system modifications such as damming, pollution, exploitation for buttons and pearls, urbanization, and the introduction and subsequent effects of aquatic invasive species. In the Great Lakes basin, the introduction of dreissenid mussels in the 1980s caused catastrophic declines, with remnant populations restricted to lotic riverine habitats. In southwestern Ontario, the Canard River is the largest remaining direct tributary of the Detroit River that could provide a source of mussels to aid natural recovery. In 2019, nine sites in the Canard River were sampled using a timed-search approach (4.5 person-hours/site) with a combination of tactile searching by hand and mussel scoops (7-mm mesh) or underwater viewers. The search yielded 362 individuals of eight species, including two Species at Risk, Mapleleaf (Quadrula quadrula) and Lilliput (Toxolasma parvum), which had never been previously recorded in the Canard River.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".