Digital mapping techniques '02, workshop proceedings : May 19-22, 2002, Salt Lake City, Utah
Bibliographic record
Abstract
The Lake Tahoe Region, straddling California and Nevada, presents a wealth of cultural, ecological, scientific, and scenic values; it has been inhabited for at least 8,000 years.The Lake Tahoe Region is within a relatively young, large, and very deep graben.The regionʼs ecosystem is actively shaped by its geology, which includes strong tectonism and a history of recent landslides/ tsunamis around the lake.Archaeological, historical-survey, and recent scientific mapping activities document on numerous maps the regionʼs evolution.Over 3,400 names for topographic and geologic features appear on these maps.In this paper, we describe the development of a geologically sophisticated gazetteer service, the Tahoe Regional Gazetteer (TARGA), which interrelates feature names with geologic maps.In conjunction, TARGA has built an inventory of 69 data sets, including 16 geologic maps, for the LTR, accumulated into a standardized repository.All three of TARGAʼs component subsystemsinventory, repository, and gazetteer-are Web accessible and Web mapped, providing convenient answers to such questions as: What geological maps exist for cultural and/or physical feature(s) "X"?In addition to its online capabilities, TARGA has accumulated a valuable database for future research on the geology of the LTR, and for geologic-map data-management systems in general.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.028 |
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".