MétaCan
Menu
Back to cohort
Record W7135816652

Spatial orientation inestigation

2014· dissertation· cs· W7135816652 on OpenAlexaboutno aff
Martina Rebcová

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2014
Typedissertation
Languagecs
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsnot available
Fundersnot available
KeywordsOrientation (vector space)Age groupsSpatial abilitySpatial cognitionTriangulationTest (biology)CognitionSpatial relation
DOInot available

Abstract

fetched live from OpenAlex

Goal: The goal of the study is to find out whether increasing age is related to the level of spatial orientation disorder. The study summarizes a current knowledge about spatial orientation, both from the point of view of neurophysiology and aging, and its investigation. We research and compare two different age groups with the aid of three tests. The issue of physiological aging that potentialyl causes a disorder of spatial orientation is discussed in the experimental part of the thesis. Hypothesis: Increasing age affects spatial perception. Methods: We used Montreal cognitive test to select mentally healthy participants, and divided them into two different age groups. The group A is composed of 10 people aged from 75 to 85, their average age is 77 years. 10 people aged from 18 to 25 fall into the group B, their average age is 23 years. These particular groups were tested by Triangulation test and so-called "follow the route" test. Results: The complex of apllied tests confirmed the difference between group A and group B. There is a connection between physiological aging and lowered capability of spatial navigation. Conclusion: The results prove a change of spatial orientation due to physiological aging. Powered by TCPDF (www.tcpdf.org)

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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 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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueDigital Repository (National Repository of Grey Literature)Same topicSpatial Cognition and NavigationFrench-language works237,207