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Record W4391820633 · doi:10.36371/port.2023.special.9

Spatial Information Systems for The Security And Military Aspects

2024· article· en· W4391820633 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal Port Science Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsComputer scienceInformation securityComputer securitySpatial analysisGeographyRemote sensing

Abstract

fetched live from OpenAlex

The use of technological inputs in collecting, storing, retrieving, displaying, manipulating, managing, and analyzing spatial information is referred to as location information system technology. It combines Remote Sensing, Geographical Information Systems, Digital Cartography, and Database Management Systems. Mapping all types of data is very important for security risk and battle filed management. The concentration will be on two fields. The first one is Security monitoring and Sensors Network applications that required the assistance with Geographical Information System (GIS). Offering the ability to modify the spatial and time data of this paper we will focus on the security risk surveillance, in accordance with GIS map elements. The second filed is the Military assets that use GIS for a variety of purposes such as cartography and intelligence. This paper will focus on battle field management, landscape analysis, military installation management, and other topics.  Interpreting, and visualizing the data that comes from those remote sensors.

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.

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)0.033
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.002
Scholarly communication0.0020.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.419
Teacher spread0.354 · 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