TREND: United States Census Bureau. Federal Aid and Assistance [Archive]: Total Funds | State: Alabama | Recipient*: All Recipients | Federal Program Name: WILDLIFE RESTORATION, 1997 - 2010. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 001-039-003
Bibliographic record
Abstract
United States Census Bureau. Federal Aid and Assistance [Archive]: Total Funds | State: Alabama | Recipient*: All Recipients | Federal Program Name: WILDLIFE RESTORATION, 1997 - 2010. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 001-039-003 Dataset: Total funding from Federal and non-Federal sources for 600 Federal programs, as identified by the Catalog of Federal Domestic Assistance (CDFA). The Census Bureau's Financial Assistance Awards Data System (FAADS) contains data on financial assistance awards under approximately 600 federal programs, as identified by the Catalog of Federal Domestic Assistance (CDFA) While primarily concerned with assistance to state and local governments, all major programs providing transfer payments to individuals, discretionary project grants, loans, or insurance are covered. FAADS was created to meet statutory requirements for program-level review by state and local governments and by the Congress. Data from FAADS is included along with other data in the Consolidated Federal Funds Report (CFFR), which is also prepared by Census Bureau. https://www.census.gov/govs/www/faads.html Category: Government and Politics Subject: State Government, Local Government, Government Spending, Social Insurance Systems Source: United States Census Bureau The US Census Bureau is a bureau of the US Department of Commerce. The major functions of the Census Bureau are authorized by Article 2, Section 2 of the United States Constitution, which provides that a census of population shall be taken every 10 years, and by Title 13 and Title 26 of the United States Code of Federal Regulations. The Census Bureau is responsible for numerous statistical programs, including census and surveys of households, governments, manufacturing and industries, and for US foreign trade statistics. The first US census was conducted in 1790 for the purposes of apportioning state representation in the US House of Representatives and for the apportionment of taxes. http://www.census.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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.025 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.113 | 0.130 |
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".