On the Integration of Regional Classification and Delineation Systems into <i>The National Map</i>
Bibliographic record
Abstract
Many of the qualities that characterize geographic regions are vague and granular in their nature. In many quality-based classification and delineation systems for geographic regions, therefore, there is a trade-off between the possible precision of the quality-based delineation and the scientific sophistication of the quality-based classification of geographic regions. This poses a dilemma for the US Geological Survey's National Map, whose purpose is to provide various integrated classification and delineation systems that can serve a wide range of users. Some users need precise delineation systems, while others need sophisticated classification systems. Many users are required to use and to produce data that are not affected by the above trade-off and that can be integrated in consistent ways. This article discusses an ontology-based solution to this problem, presented in the specific context of systems for classifying and delineating eco-regions and eco-zones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".