Predicting Suitable Habitat for <i> Sk <sup>w</sup> enk <sup>w</sup> ínem </i> ( <i>Claytonia lanceolata</i> ), a Culturally Significant Plant, Using a Reproducible Species Distribution Model
Bibliographic record
Abstract
ABSTRACT Colonialism and a changing climate have threatened culturally significant food plants and the well‐being of those who rely on them. An example is sk w enk w ínem (western spring beauty, Claytonia lanceolata : Pursh), which has declined in quality and accessibility for the Secwépemc People of Skeetchestn Indian Band. This study aims to provide predictions and visual tools to inform Skeetchestn's conservation efforts for their sk w enk w ínem patches, from a computationally reproducible species distribution model. Using a set of predictors informed by qualitative interviews with Skeetchestn community members (Informed model), and the 19 bioclimatic variables from WorldClim (WorldClim model), we predicted suitable habitat for sk w enk w ínem over its known range. For both our total study area and Skeetchestn Territory, we predicted a decrease in suitable habitat from the present to 2081–2100, based on the WorldClim CMIP6 climate change scenario SSP 5–8.5 (worst case). These predictions use Skeetchestn's knowledge of sk w enk w ínem and commonly used bioclimatic predictors to support Skeetchestn's goals of food sovereignty.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".