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Record W4404842964 · doi:10.3791/66833

Orienteering as a Tool for Cognitive Research: An Implementation Guide

2024· article· en· W4404842964 on OpenAlexaff
Emma E. Waddington, Jennifer J. Heisz

Bibliographic record

VenueJournal of Visualized Experiments · 2024
Typearticle
Languageen
FieldComputer Science
TopicCognitive Science and Mapping
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOrienteeringComputer scienceCognitionCognitive scienceProcess managementHuman–computer interactionData sciencePsychologyNeuroscienceEngineeringMathematics

Abstract

fetched live from OpenAlex

The sport of orienteering combines physical activity with spatial navigation. Using only a map and a compass, the orienteer must locate a series of checkpoints over unfamiliar terrain using any navigational route they choose and while moving as quickly as possible. Although expert orienteers have superior spatial memory and navigational abilities, even a single session of orienteering can benefit cognition, suggesting that orienteering may be a promising way to train the brain. Research interventions involving orienteering may be especially beneficial for staving off Alzheimer's disease and related dementias that are afflicted by early impairments in wayfinding and spatial cognition. Though orienteering has gained traction in recent literature, certain barriers exist for researchers who are unfamiliar with the sport and wish to implement an intervention. Specifically, a lack of research-based resources for creating orienteering maps and courses may prevent those wishing to study orienteering from designing an intervention. Therefore, this report provides the fundamental information needed to develop orienteering maps and courses and how to implement orienteering interventions in a research setting.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.608
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.172
GPT teacher head0.594
Teacher spread0.423 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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