Implementation of Bayesian Inference Technique to Address Data Limited Problems in Acology: A Case study with Peary Caribou in Canadian Arctic Archipelago
Bibliographic record
Abstract
In the present era, rates of decline in species’ abundance provide some of the most compelling evidence of biodiversity loss rates globally. To address the problem of biodiversity loss, a critical piece of knowledge is the understanding of species interactions with their environment, because environmental variables are generally better predictors of population integrity than intrinsic biological traits. Peary caribou (Rangifer tarandus pearyi), the smallest of all caribou subspecies, are endemic to the Canadian Arctic Archipelago (CAA) and a characteristic example of species at risk. Climate change can affect their habitat availability, as well as the makeup of the entire Arctic ecosystem. Logistical and financial constraints in the CAA often compromise the frequency and the spatial extent of Peary caribou surveys, and therefore inconsistent sampling, errors in measurements, or faults in data acquisition encumber the robust assessment of their population status. To remedy such data gaps in surveys and, improve the robustness of any modelling exercise, I first developed a regression-based imputation framework to reconstruct the Peary caribou time series. The model was able to capture more than 65% of the variability in the dataset. To date, little work has been done to evaluate the net impact of changes from the climate on Peary caribou population dynamics, as it has been argued that the net balance of limited forage accessibility due to severe weather conditions relative to that of increased forage biomass due to prolonged growing season will depend on local climate, floral abundance and composition, and landscape characteristics. Using a two-pronged modelling approach, I characterized the year-to-year variability of the habitat conditions across the CAA, using meteorological variables, landscape features, and resource competition. My dissertation also introduced a spatially explicit modelling framework to examine the strength and nature of the relationships of snow density and vegetation with Peary caribou populations. My dissertation concludes by identifying critical augmentations of the available scientific knowledge that necessitate to design the optimal management actions of Peary caribou populations across the Canadian Arctic Archipelago.
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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 | 0.024 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".