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Record W7064453719

Border Security: Enhanced DHS Oversight and Assessment of Interagency Coordination Is Needed for the Northern Border

2010· report· en· W7064453719 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of North Texas Digital Library (University of North Texas) · 2010
Typereport
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsnot available
FundersU.S. Department of AgricultureU.S. Department of Homeland SecurityU.S. Department of JusticeUnited States Drug Enforcement AdministrationU.S. Department of Defense
KeywordsHomeland securityBorder SecurityAccountabilityGovernment (linguistics)Northern territoryWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

A letter report issued by the Government Accountability Office with an abstract that begins "The challenges of securing the U.S.-Canadian border involve the coordination of multiple partners. The results of the Department of Homeland Security's (DHS) efforts to integrate border security among its components and across federal, state, local, tribal, and Canadian partners are unclear. GAO was asked to address the extent to which DHS has (1) improved coordination with state, local, tribal, and Canadian partners; (2) progressed in addressing past federal coordination challenges; and (3) progressed in securing the northern border and used coordination efforts to address existing vulnerabilities. GAO reviewed interagency agreements, strategies, and operational documents that address DHS's reported northern border vulnerabilities such as terrorism. GAO visited four Border Patrol sectors, selected based on threat, and interviewed officials from federal, state, local, tribal, and Canadian agencies operating within these sectors. While these results cannot be generalized, they provided insights on border security coordination."

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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
Published2010
Admission routes1
Has abstractyes

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