Mapping Species At Risk and Of Cultural Value
Bibliographic record
Abstract
The escalating urbanization and human-induced land use changes have precipitated a global biodiversity crisis, imperiling indigenous ecosystems and cultural heritage. Nanaimo Regional District (NRD) in British Columbia, with its rich biodiversity and Indigenous lands, is facing the challenge of balancing urban development and species conservation. To provide better insights for urban planners and indigenous communities on protecting local biodiversity, the study aims to visualize the biodiversity of the identified species and evaluate the land protection levels by species richness and Land Cover Species Importance Score (LCSIS). The study assigns value to two criteria by importance level to gain the SIS of the identified species at risk and of cultural value. Integrating species occurrence data with land parcels and land cover data to illustrate spatial patterns of species richness and importance level. It reveals that larger rural parcels have higher species richness, primarily located in the eastern part of NRD. Smaller parcels around urban areas, particularly east coastal regions, have higher species richness density. LCSIS value varies across different land cover types. By reclassifying and combining the species richness and LCSIS, the spatial distribution of identified Protection Areas (PAs) is mapped and classified into three classes, high, median, and low. The study also explores the proximity of different classes of PAs to urban areas, to assess if further urban expansion would impact the identified PA. Implications for urban planning are profound. By delineating priority conservation areas and integrating them into land use plans, planners can mitigate the adverse impacts of urban expansion on biodiversity and cultural heritage. This proactive approach fosters sustainable development, preserves vital ecosystems, and honors Indigenous traditions.
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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 | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".