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Record W6901589299 · doi:10.6068/dp1637498888d84

TREND: United States Census Bureau. Household Income - Median: Median Household Income | State: Michigan | County: Macomb, Oakland, Ottawa, 1997 - 2016. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 001-006-001

2018· other· en· W6901589299 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCensusPovertyPopulationHousehold incomeAmerican Community SurveyMedian incomePoverty thresholdPersonal income

Abstract

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United States Census Bureau. Household Income - Median: Median Household Income | State: Michigan | County: Macomb, Oakland, Ottawa, 1997 - 2016. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 001-006-001 Dataset: Shows median household income, by state and county. The United States Census Bureau estimates income statistics as part of its Small Area Income and Poverty Estimates (SAIPE) program. This program was created by the Census Bureau with support from other federal agencies, and provides more current estimates of selected income and poverty statistics than the most recent decennial census. Model-based estimates are created for States, counties, and school districts. The main objective of SAIPE is to provide updated estimates of income and poverty statistics for the administration of Federal programs and the allocation of federal funds to local jurisdictions. Estimates are modeled using a variety of data sources, including summarized information from individual federal income tax returns, and population information. https://www.census.gov/programs-surveys/saipe/data/datasets.html Category: Population and Income Subject: Households, Household Income 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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.892
Threshold uncertainty score0.470

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.021
Science and technology studies0.0010.000
Scholarly communication0.0020.004
Open science0.0030.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.1080.102

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.

Opus teacher head0.029
GPT teacher head0.273
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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