New Methodologies and Approaches for Mapping Forested Karst Landscapes, Vancouver Island, British Columbia, Canada.
Bibliographic record
Abstract
Mapping is an essential tool for land management and is typically used to assess the nature and characteristics of a land surface, along with its resource features and values. Mapping of karst landscapes is of particular importance for the temperate rainforests Vancouver Island on the west coast of British Columbia (BC), where both forestry and natural resource development activities occur. A set of BC Government standards for mapping karst have been developed at varying scales (reconnaissance, planning-level and detailed), and incorporate procedures to assess the potential and vulnerability of karst, applying qualitative analysis of various surface and subsurface karst attributes. Previously, karst maps have been compiled by GIS mapping software and generated as static graphic images. However, it is now possible to make maps more accessible and interactive by uploading them as overlays within Google Earth (as KML files) or other imagery platforms. It is also possible for these maps to be taken into the field using tablets, phones or iPads, allowing for on-site data collection and resource evaluation. A key focus of this research is the development of a `Karst Map of Vancouver Island’ that outlines the known extent of the karst and the likely contributing non-karst catchments, and also identifies regions of varying karst potential/vulnerability and notable karst areas/features. Trials have been completed to see how detailed imagery of karst features and areas collected by UAV technology can be used for karst evaluation. Karst areas have also been mapped using LiDAR, which has the potential for both detecting surface karst features beneath the forested canopy and for assessing the overall sensitivity of forested karst areas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".