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Record W7132940313

Effects of caffeine on exercise performance and subsequent target detection and rifle marksmanship under simulated combat conditions

2003· dissertation· W7132940313 on OpenAlexfundno aff
Robin Lynn Gillingham

Bibliographic record

VenueTSpace · 2003
Typedissertation
Language
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsnot available
FundersArmy Research Institute for the Behavioral and Social SciencesU.S. ArmyMinistère de la Défense Nationale
KeywordsRifleVigilance (psychology)PlaceboPoison controlTime trialAffect (linguistics)Session (web analytics)Perceived exertion
DOInot available

Abstract

fetched live from OpenAlex

Twelve reservists ingested 5.0 mg/kg of caffeine (C) or placebo (P) one hour before beginning a 2.5 h loaded march and 1.0 h sandbag wall construction task. Following exercise, participants were given a redose of 2.5 mg/kg of C or P. One hour postingestion, participants commenced a 2.5 h shooting session on a small arms trainer, which included friend-foe (FF) and vigilance (VIG) tasks. In total, four counterbalanced dose-redose experimental trials were conducted: PP, PC, CP, and CC. Performance measures during the shooting session included the number of shots taken (NS), engagement time (ET), and marksmanship. Results indicated that C did not affect exercise or shooting performance during the FF task. NS and ET improved with CC during the VIG task. In conclusion, C improves target detection and engagement speed during vigilance situations, but is not effective during more complex operations requiring dual task proficiency.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.336
Teacher spread0.319 · 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 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

Citations0
Published2003
Admission routes1
Has abstractyes

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