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Record W4392633742 · doi:10.36371/port.2023.special.14

Analysis and Improvement of Geographic Information Systems for Problem Solving and Decision Making

2024· article· en· W4392633742 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 scienceGeographic information systemManagement scienceData scienceInformation systemGeographyEngineeringRemote sensing

Abstract

fetched live from OpenAlex

The complexity of our way of life has increased due to the numerous parts of life developing quickly, which results in ongoing issues. To address these issues and respond to these rapid changes in the environment, solutions that were practical, quick, and easy to implement were needed. The use of geographic information tools, a contemporary innovation, allows for the implementation of difficult issues by enhancing users' abilities to comprehend problems thoroughly through the analysis of spatial data and the creation of digital maps. This allows decision-makers to save time, effort, and money by making informed choices that will result in the best possible solution to the issue at hand. In addition to a significant urban expansion, an increase in the number of people using vehicles, and a significant emigration of people from rural and small towns, the traffic jams that have recently engulfed much of the world, particularly in the major capitals and cities, are a result of these factors for transportation, have contributed to a complex problem in modern times. The goal of this study is to examine proposed elements and gauge their impact on the issue of traffic bottlenecks. It then suggests both long- and short-term remedies for this issue based on the study's findings, which are generally not found in the pertinent departments. In order to create a unique surface for these components and provide a comprehensive picture of the research region, it also makes satellite photos of the area available. geographical database for each of them, evaluate each layer's influence on the traffic jam independently by analyzing it, and then merge these levels to create full map that shows their location and the extent to which they affect the study problems. This process was done in order to arrive at the aforementioned solution.

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)0.029
metaresearch head score (Gemma)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0020.001
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 head0.048
GPT teacher head0.416
Teacher spread0.369 · 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