Preliminary integrated geologic map databases for the United States: Digital data for the reconnaissance geologic map of the lower Yukon River region, Alaska
Bibliographic record
Abstract
The growth in the use of Geographic Information Systems (GIS) has highlighted the need for digital geologic maps that have been attributed with information about age and lithology. Such maps can be conveniently used to generate derivative maps for manifold special purposes such as mineral-resource assessment, metallogenic studies, tectonic studies, and environmental research. This report is part of a series of integrated geologic map databases that cover the entire United States. Three national-scale geologic maps that portray most or all of the United States already exist; for the conterminous U.S., King and Beikman (1974a,b) compiled a map at a scale of 1:2,500,000, Beikman (1980) compiled a map for Alaska at 1:2,500,000 scale, and for the entire U.S., Reed and others (2005a,b) compiled a map at a scale of 1:5,000,000. A digital version of the King and Beikman map was published by Schruben and others (1994). Reed and Bush (2004) produced a digital version of the Reed and others (2005a) map for the conterminous U.S. The present series of maps is intended to provide the next step in increased detail. State geologic maps that range in scale from 1:100,000 to 1:1,000,000 are available for most of the country, and digital versions of these state maps are the basis of this product. The digital geologic maps presented here are in a standardized format as ARC/INFO export files and as ArcView shape files. Data tables that relate the map units to detailed lithologic and age information accompany these GIS files. The map is delivered as a set 1:250,000-scale quadrangle files. To the best of our ability, these quadrangle files are edge-matched with respect to geology. When the maps are merged, the combined attribute tables can be used directly with the merged maps to make derivative maps.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".