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Record W2745873091 · doi:10.13034/jsst.v10i1.126

Importing Data from Shapefiles and Pathfinding along Generated Nodes

2017· article· en· W2745873091 on OpenAlexvenueaboutno aff
Victor Brestoiu

Bibliographic record

VenueJournal of Student Science and Technology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsShapefilePathfindingComputer scienceCartographyNode (physics)GeographyShortest path problemWorld Wide WebGraphTheoretical computer scienceMetadataEngineering

Abstract

fetched live from OpenAlex

The Shapefile format is a particular standard for storing GIS (Geographic Information System) data, designed and developed by the Environmental Systems Research Institute (ESRI). The purpose of this project was to extract the binary data describing the City of Lethbridge from ESRI Shapefiles, and then to demonstrate an ability to utilize and modify this data. The utilization component centered on pathfinding and visually drawing the data, while the modification component involved the creation of a new, human-readable file type which contained the processed Shapefile data. These goals were accomplished by converting the Shapefile data into custom ‘Node’ objects in C++ code. These nodes form the basis for further development, as more attributes can easily be added to them as needed. The implemented pathfinding is a matter of picking a starting and ending node, and travelling across their adjacent nodes until a shortest path is found, a search algorithm called A* (read: A Star). Although further work is necessary for a robust product, this platform is already highly modular and is freely available open source. Le format Shapefile est un standard particulier pour le stockage des données du système d’information géographique (SIG), conçu et développé par l’Institut de Recherche des Systèmes Environnementaux (ESRI). Le but de ce project était d’extraire les données binaires qui décrivent la ville the Lethbridge des Shapefiles ESRI, et de démontrer que ces données peuvent être utilisées et modifiées. Le composant d’utilisation était centré sur la navigation et la visualization des données, tandis que le composant de modification a demandé la création d’un nouveau format lisible aux humains qui contient les données Shapefile traitées. Ces buts ont été accomplies en convertissant l’information Shapefile en objets ‘nœud’ personnalisés dans le langage de programmation C++. Ces nœuds forment la base pour les développements plus approfondis, car plus d’attributs peuvent être facilement ajoutés aux nœuds lorsque nécessaire. Le système de navigation implémentée est alors une question de choisir un nœud de départ et de terminaison, puis voyager à travers leurs nœuds adjacents jusqu’à la découverte de la route la plus courte. Ce procès informatique est l’algorithme de recherche A* (lu : A Star). Quoi qu’encore plus de travail soient nécessaire pour le développement d’un produit able, cette plateforme est déjà très modulaire et disponible à l’open-source.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0300.016

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 head0.084
GPT teacher head0.391
Teacher spread0.307 · 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 designSimulation or modeling
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

Citations1
Published2017
Admission routes2
Has abstractyes

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