Canadian Arctic vegetation mapping
Bibliographic record
Abstract
During the next few decades the Arctic is expected to experience unprecedented changes in climate and resource development. All of these will potentially affect land use and vegetation cover. There is a need for a comprehensive and consistent circumpolar map of arctic vegetation that will be useful for modelling vegetation change in the circumpolar region. The Canadian arctic vegetation map is part of the Circumpolar Arctic Vegetation Mapping project (CAVM) which was initiated to fulfil this need. The CAVM is an effort by an international group of arctic vegetation scientists to create a map and GIS database of circumpolar vegetation at the 1:7 500 000 scale. The Canadian vegetation map and ultimate circumpolar map will be useful for the study of arctic vegetation, modelling vegetation change at the continental and circumpolar scale, interpreting patterns of wildlife distribution and migration, land management, and educational purposes. The mapping effort combines information on soils, bedrock and surficial geology, hydrology, remotely-sensed vegetation characteristics, previous vegetation studies and regional expertise of mapping scientists. Map units are drawn using photo-interpretation of a 1:4 000 000 scale AHVRR false colour infrared image basemap. Mapped polygons represent homogeneous landscape terrain units (e.g. hills, plains, plateaus, mountains and valleys). A GIS database contains ancillary information for each polygon and vegetation is defined through a series of lookup tables with information on dominant climatic, parent material chemistry and topographic characteristics. We present the mapping methods, a vegetation map of the Canadian Arctic, and ancillary maps developed in the mapping process. Twenty land cover classes are presented on the map, including 17 vegetation classes that are defined by dominant physiognomy (growth form), dominant moisture regime, characteristic plant communities and characteristic degree of vegetation cover. Ancillary data presented include the AVHRR CIR basemap and landscape unit polygons, a maximum NDVI image, bioclimatic and elevational zones, and a map of parent material pH.
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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 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".