Bibliographic record
Abstract
Vulnerability of property values of Texas to floods according to the property values and the density of those property values of each Texas Census Block. The categories are ranged from high to low. Data was created by combining the values of property contents and building valuations from the HAZUS-MH inventory database. This data set provides the content valuation and the building valuation for each HAZUS specific occupancy classifications. All data was developed at the census block level for the United States in the fifty states, the District of Columbia, and the territories. ABSG developed this data set from the 2000 version of TIGER/Line files and first quarter of 2002 data from D&B. The dataset was generated though the application of proportions of contents to building value over the total building value for each specific occupancy, and by applying RS Means replacement values for typical building floor areas and construction for each specific occupancy. The contact information for the Census Bureau is: U.S. Department of Commerce, U.S. Census Bureau, Geography Division. 8903 Presidential Parkway, Room 303 WP I, Upper Marlboro, Maryland, 20772. Telephone: (301) 457-1128. E-Mail Address: tiger@census.gov. The U.S. Census Bureau website address is http://www.census.gov/geo/www/tiger/index.html. The contact information for Dun & Bradstreet is: Dun & Bradstreet, 3 Sylvan Way, Parsippany, New Jersey 07054. Telephone (800) 526-0651. The D&B website address is http:///www.dnb.com. The contact information for RS Mean's is: RSMeans Company, Inc. Construction Publishers & Consultants, Construction Plaza, 63 Smiths Lane, Kingston, MA 02364-080. Telephone: (781) 585-7880. The RSMeans website address is: http://www.rsmeans.com/.
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 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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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; both teacher heads agree on what is shown here.
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".