Bibliographic record
Abstract
Research, conservation, and effective natural resource management often depend on maps that characterize vegetation patterns. Quantitative and ecologically specific representations of vegetation pattern better represent observed vegetation patterns than do traditional categorical vegetation maps. They also avoid a human interpretational bias not necessarily shared by or important to plants or wildlife. We developed quantitative continuous foliar cover maps for 16 plant and lichen species or ecologically narrow aggregates in arctic and boreal Alaska and adjacent Yukon (North American Beringia). To provide context to the performance of our continuous foliar cover maps, we compared our results to the performances of three categorical vegetation maps that cover arctic and boreal Alaska: the National Land Cover Database, the coarse classes of the Alaska Vegetation and Wetland Composite, and the fine classes of the Alaska Vegetation and Wetland Composite. We integrated new and existing ground and aerial vegetation observations for arctic and boreal Alaska from three vegetation plots databases. To map patterns of foliar cover, we statistically associated observations of vegetation foliar cover with environmental, multi-season spectral, and surface texture covariates using a Bayesian statistical learning approach. Our maps predicted 33% to 67% of the observed variation in foliar cover per map class at a 10 × 10 m resolution, although the accuracy of each map varied between the Arctic, Southwest, and Interior subregions. We show that while some maps have high noise at the 10 × 10 m resolution, they generally capture vegetation patterns accurately at local to landscape scales. All continuous foliar cover maps performed substantially better than the categorical vegetation maps both for the entire region and for all subregions. The vegetation database and scripted workflow that we developed to create the continuous foliar cover maps will allow consistent future annual or semi-annual updates to include new observations of vegetation patterns and new covariate data. Our scripted workflow will also allow the application of our methods to different areas. Our continuous foliar cover maps extend knowledge of the functional role of vegetation in communities and wildlife habitat in North American Beringia beyond what has been previously available in categorical vegetation maps or quantitative maps of broadly defined vegetation aggregates.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".