From the redwood forest to the Gulf Stream waters: human signature nearly ubiquitous in representative US landscapes
Bibliographic record
Abstract
What landscapes best represent the land uses and land covers (LU/LC) of the continental United States? Would the set include a cornfield? A forest? A backyard? Combining principles of landscape ecology and computer science, we identified a small set of “exemplar landscapes”, representing distinct LU/LC pattern types of the conterminous US. We first partitioned the 1992 US National Land Cover Dataset into 193 705 landscapes, and quantified patterns with standard measures of LU/LC composition and configuration. Using the values to estimate similarity of LU/LC patterns between landscapes, we applied an algorithm developed to find representatives in large sets. In the resulting 17‐member set of exemplar landscapes, patterns created and managed by human activity are by far the most evident features. This set of representatives summarizes the nation's LU/LC, demonstrating the degree to which human‐influenced patterns dominate: aggregations of rectangular fields, farmlands within cleared forests, shrublands/pasture, and suburbs. The algorithm's selection of an exemplar for each group may have other ecological applications when an objectively determined subset of representative items is needed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".