EnviroAtlas - Portland, ME - Meter-Scale Urban Land Cover (MULC) Data (2010)
Bibliographic record
Abstract
the enviroatlas portland or meter scale urban land cover mulc dataset includes data for the portland metropolitan area plus the city of vancouver washington and various smaller towns and rural areas in oregon and washington the total area classified was approximately 2160 square kilometers the land cover data were generated from 1 m four band red green blue and near infrared aerial photography acquired from the united states department of agriculture s national agriculture imagery program imagery for oregon was collected in 2012 and imagery for washington was collected in 2011 in addition ancillary datasets were derived for the classification from two lidar datasets collected in 2007 and one lidar dataset collected in 2010 eight land cover classes were mapped water impervious surfaces soil and barren land trees and forest grass and herbaceous non woody vegetation agriculture and wetlands both woody and emergent an accuracy assessment using 600 completely random and 54 stratified random land cover reference points yielded an overall accuracy of 78 6 using a liberal interpretation with similar classes e g soil grass soil agriculture the overall fuzzy accuracy is 91 4 for more information on fuzzy accuracy assessment see the overview section this dataset was produced by the us epa to support research and online mapping activities related to enviroatlas enviroatlas https www epa gov enviroatlas allows the user to interact with a web based easy to use mapping application to view and analyze multiple ecosystem services for the contiguous united states the dataset is available as downloadable data https edg epa gov data public ord enviroatlas or as an enviroatlas map service additional descriptive information about each attribute in this dataset can be found in its associated enviroatlas fact sheet https www epa gov enviroatlas enviroatlas fact sheets
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.031 |
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".