Bibliographic record
Abstract
Conserving biodiversity is suggested to be one of the most important challenges being faced by the global community. The field of conservation biology has been developed to examine the threats that drive species to low abundance, the dynamics of species in low abundance and the methods to rebuild abundance. Typically, assessing these issues requires substantial data inputs; however we are often faced with situations where little information exists. In this thesis, I addressed several key conservation priorities in the endangered Atlantic Whitefish (Coregonus huntsmani), a data poor species, which has been restricted to one watershed for most of the past century.\nUsing molecular genetic markers Atlantic Whitefish were determined to be a distinct and basal species within the genus. Population size was suggested to be low and the incidence of inbreeding high as genetic effective population size was among the lowest of any fish species examined and genetic diversity was 2-6 times lower than regional congeners.\nThrough laboratory experiments environmental threats to the persistence of Atlantic Whitefish were examined. Overall, Atlantic Whitefish were tolerant to a broad range of environmental conditions and were capable of surviving in harsher environments than many other regional species. Furthermore, their persistence in current habitats will likely not be influenced by the assessed environmental conditions. As part of this work, a suite of methods and metrics to compare thermal sensitivity across a range of finfish species were assessed.\nIn order to inform recovery efforts, I developed simulation models to evaluate habitat suitability for translocation of Atlantic Whitefish. As part of this work, I examined the role of incorporating variability in species response, environment and / or life history into simulations. The results showed that the inclusion of multiple sources of variability altered the perception of optimal habitats; however, several watersheds offered suitable translocation habitats.\nThroughout this thesis I explored novel tools to address some of the key issues facing conservation programs of data poor species. This work is not only applicable to the conservation of Atlantic Whitefish, but also outlines some of the potential tools useful in addressing conservation priorities in other species.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".