Using Harmonization to Examine Freshwater Mussel Species at Risk: A Sydenham River Watershed Case Study
Bibliographic record
Abstract
There are over 300 species of freshwater mussels (Family: Unionidae) across North America with many populations at risk or in decline. Mussels provide essential ecosystem services, yet they remain largely underexplored in terms of distribution, population sizes, life history traits, and co-existence with hosts, making species conservation efforts a challenge. To advance the conservation of freshwater mussel species at risk of extinction (SAR), this thesis aims to extend understanding of freshwater mussel SAR distributions in a biodiverse but vulnerable watershed in the Laurentian Great Lakes basin. This thesis asks: How can freshwater mussel SAR distribution data be leveraged through existing collaborations and other available knowledge? Further, how can we address uncertainty in freshwater mussel distributions through a better understanding of their host fish requirements? My work was conducted in the Sydenham River watershed, which is the most biodiverse in Canada with 35 different mussel species, 14 of which are federally listed as at risk of extinction. I employed multiple research methods including: an empirical field survey of existing mussel assemblages and environmental conditions across the watershed, a literature synthesis to compile all available host fish data, and expert input to refine local host information for the watershed. In my empirical survey, I found two major patterns in assemblages across the watershed: habitats within the main stem East branch of the watershed were significantly richer and significantly different based on environmental characteristics than the North branch. Additionally, host fishes were not very good predictors for where SAR freshwater mussels reside. Further, habitat characteristics informed mussel assemblage composition. My results offered multiple lines of evidence to demonstrate that harmonizing available datasets can help better understand mussel communities and therefore be applied to watershed-scale restoration efforts in the Great Lakes and beyond.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".