Modelling Whitebark Pine Distribution in the Crown of the Continent Ecosystem
Bibliographic record
Abstract
Whitebark Pine (Pinus albicaulis; WBP) is a species facing serious threats throughout its range. To support ongoing WBP conservation efforts in the Crown of the Continent Ecosystem (CCE), this thesis builds a multi-jurisdictional species distribution model (SDM). There are many critical decisions in building SDMs and little consensus on what works best. In this thesis, I consider how parameters of model type, pseudo-absence selection methods, and the number of pseudo-absences affect model performance. To deal with imperfect input data, I first build and test these model parameters on five WBP-like virtual species (VS). The results show that there are many model parameter combinations that perform equally well, but they predicted different spatial patterns of occurrence. Ensemble models (EM) were used to combine the information in these models into a final EM that outperforms any single model. Building a number of simple and ecologically sound models to use in an EM may be more useful than searching for the best single model. Whether VS are suitable proxies for real species is still unknown, but, used with caution, they have the potential to inform modelling parameter choices in circumstances where input data are imperfect. The final model built in this study is suitable for understanding the broad-scale relative probability of WBP across the CCE, and should complement finer scale work.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".