MétaCan
Menu
Back to cohort
Record W2607914597 · doi:10.3389/frym.2017.00010

When Kicking the Doctor Is Good—A Simple Reflex

2017· article· en· W2607914597 on OpenAlexafffund
Jennifer M. Jakobi, Sienna Kohn, Samantha Kuzyk, Andrey Fedorov

Bibliographic record

VenueFrontiers for Young Minds · 2017
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReflexNeuroscienceAnkle jerk reflexWithdrawal reflexSpinal cordSimple (philosophy)H-reflexTriceps reflexMedicineComputer scienceAnatomyPsychologyPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Humans and animals have reflexes to help protect them from danger. Reflexes are unconscious responses, which means they are automatic and do not require the brain to create the action. There are many different types of reflexes, but the most basic is called a simple reflex. A simple reflex contains only one space where information in the spinal cord travels between two nerve cells, called neurons. The space between two neurons is called a synapse. Thus, a simple reflex is called monosynaptic, where “mono” means “one.” There are four parts to a monosynaptic simple reflex. The first is a sensor, which senses what is happening to the body, the second is a sensory neuron to carry that information to the spinal cord, and the third is a motor neuron to transmit information away from the spinal cord to the fourth part, which is the muscle that creates an action. Doctors will test reflexes by tapping the tendon just below the knee, and this causes the leg to kick out. This knee-jerk reflex is an example of a simple monosynaptic reflex.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.065
GPT teacher head0.326
Teacher spread0.261 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers for Young MindsSame topicTranscranial Magnetic Stimulation StudiesFrench-language works237,207