TREND: United States Department of Veterans Affairs. Veterans Population: Veteran Deaths | State: Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, Washington, Washington DC, West Virginia, Wisconsin, Wyoming | Age: All Ages | Gender: All Genders, 2000 - 2030. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 033-001-002
Bibliographic record
Abstract
United States Department of Veterans Affairs. Veterans Population: Veteran Deaths | State: Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, Washington, Washington DC, West Virginia, Wisconsin, Wyoming | Age: All Ages | Gender: All Genders, 2000 - 2030. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 033-001-002 Dataset: Deaths of veterans over specific periods of time. Data are from the Social Security Death File, the Veterans Affairs Compensation and Pension File, and Defense Manpower Data Center (DMDC) data (for actual deaths of those veterans who separated from DOD during the time frame of report). NOTE: As of 2013, the Veterans Administration is no longer collecting this data. The Department of Veterans Affairs (VA) provides official estimates and projections of the veteran population using the Veteran PopulationModel (VetPop). The model is updated periodically for improved methodology, more recent data, and changing needs. Data are from the VA and are based in part on State migration data from the Census Bureau, including the American Community Survey, actual separations data from the DOD, and other data from the Defense Manpower Data Center. Projections are based on projected separations from the GORGO data projection model used by the DOD's Office of Actuary. NOTE: As of 2013, the Veterans Administration is no longer collecting data on veterans' deaths. http://www1.va.gov/vetdata/ Category: Health and Vital Statistics, Military and Defense Subject: Veterans, Deaths Source: United States Department of Veterans Affairs The United States Department of Veterans Affairs (VA) operates programs to benefit veterans and members of their families. The VA was established as an executive department by the Department of Veterans Affairs Act. Veteran research and statistics are maintained by the VA Office of Policy as part of the National Center for Veterans Analysis and Statistics. http://www.va.gov/
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.058 | 0.052 |
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".