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Record W1504414399 · doi:10.21236/ada565776

CNA's Integrated Ship Database, Fourth Quarter 2011 Update

2012· report· en· W1504414399 on OpenAlexaboutno aff
Gregory N. Suess, Lynette A. McClain, Rhea Stone

Bibliographic record

Venuenot available
Typereport
Languageen
FieldComputer Science
TopicTechnology and Education Systems
Canadian institutionsnot available
Fundersnot available
KeywordsDatabaseQuarter (Canadian coin)Computer scienceHistoryArchaeology

Abstract

fetched live from OpenAlex

Abstract : In this CNA Interactive Software document, we present the update of our Integrated Ship Database (ISDB) for the third quarter of calendar year 2012. In this version of the ISDB, as in the previous ones, we bring together data on naval ships from several different online government sources into a single combined database tool. The ISDB makes ship data readily accessible for analysis and reporting. With the database, you can see the makeup of the Navy's Battleforce. You can find where ships are in their life cycle: authorized by Congress, under construction, in commission/in service, in reserve, leased or loaned, stricken from the Register, or disposed. This work is being conducted as part of CNA's Historic Fleet Employment Database Project, sponsored by the Deputy Chief of Naval Operations for Operations, Plans, and Strategy (N3/N5). Since our initial version in 2005, we have published 26 quarterly updates. They are all available online on the Integrated Ship Database page of CNA's website: www.cna.org. In the first section of this document, we present an overview of the ISDB. We tell you where to find the database online and how to access it. Then we show how the Microsoft Excel database file is organized according to worksheet tabs for documentation, database navigation, data tables, and pivot tables for display and analysis. If you are looking for details, please refer to the original 2005 user's manual and to the fourth quarter 2011 update. In addition, the documentation of the second quarter 2012 update discusses the changes we made due to the Military Sealift Command's (MSC) organizational realignment of 2012. After the overview, we discuss changes in the database because of modifications to the Naval Vessel Register (NVR), the MSC Ship Inventory, and the Maritime Administration's (MARAD) National Defense Reserve Fleet (NDRF) Inventory between July 1 and September 30, 2012. Finally, we present ship counts and inventory summaries as of September 30, 2012.

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)0.008
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.192
Threshold uncertainty score0.382

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0130.020
Science and technology studies0.0020.000
Scholarly communication0.0100.006
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0870.161

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 head0.078
GPT teacher head0.303
Teacher spread0.225 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations2
Published2012
Admission routes1
Has abstractyes

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