Statistical approaches for wildlife conservation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Statistical models are valuable tools for informing conservation practices; their outputs often guide policy decisions, identify key driving mechanisms, and shape strategy. Consequently, the demand for accessible methods that produce interpretable results has increased substantially. This thesis addresses the demand from two perspectives. First, to enable identification of sensitive or illegal behaviours which affect biodiversity, we develop a comprehensive framework categorizing all Randomised Response Techniques (RRTs) where the sensitive variable is discrete. We demonstrate how this framework unifies several classical discrete RRT designs, enabling a straightforward comparison of their properties. Building upon this, we introduce a procedure that allows researchers to design surveys optimised for efficiency while meeting specified privacy constraints. To support practical implementation, this work is accompanied by an accessible RShiny application. Second, to aid in identifying common mechanisms of change, we develop a hierarchical clustering framework that aims to balance the minimisation of within-cluster variation with the interpretability of predefined ecological groupings. We apply this framework to a large-scale dataset on Canada's bird populations. Our analysis reveals that traditional guild-level summaries miss substantial species-level variation and proposes several alternative clustering strategies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it