Bibliographic record
Abstract
Research, conservation, and effective natural resource management often depend on maps that characterize patterns of vegetation composition. Quantitative and ecologically specific representations of plant proportional abundance have several advantages: they are theoretically consistent with plant community ecology, avoid arbitrary and subjective thresholds or categorizations, and minimize information loss relative to field observations and covariates. 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 15 plant species or ecologically narrow aggregates in Arctic and boreal Alaska and adjacent Yukon (North American Beringia). 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 hierarchical statistical learning models. 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 and the coarse and fine classes of the Alaska Vegetation and Wetland Composite. Our maps predicted 40% to 62% of the observed variation in foliar cover per species or aggregate at the site scale. A multi-scale accuracy assessment showed that the maps generally captured patterns of plant abundance accurately at landscape and regional scales. All continuous foliar cover maps performed substantially better than the existing categorical vegetation maps. The vegetation database and scripted workflow that we developed to create the continuous foliar cover maps will allow consistent future updates to include new observations of plant abundance patterns and new or updated covariates. Our scripted workflow also allows the application of our methods to areas beyond North American Beringia. The continuous foliar cover maps that we developed improve representation of vegetation composition patterns relevant to plant communities and wildlife habitats in North American Beringia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".