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Record W2753395849 · doi:10.1038/s41598-017-10796-y

The syntactic organization of pasta-eating and the structure of reach movements in the head-fixed mouse

2017· article· en· W2753395849 on OpenAlexafffund
Ian Q. Whishaw, Jamshid Faraji, Jessica R. Kuntz, Behroo Mirza Agha, Gerlinde A. S. Metz, Majid H. Mohajerani

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsBitingNumerical digitGRASPOrientation (vector space)CommunicationPosition (finance)EthogramHead (geology)Computer scienceArtificial intelligencePsychologyPhysical medicine and rehabilitationBiologyMedicineMathematicsArithmeticGeometryZoology

Abstract

fetched live from OpenAlex

Mice are adept in the use of their hands for activities such as feeding, which has led to their use in investigations of the neural basis of skilled-movements. We describe the syntactic organization of pasta-eating and the structure of hand movements used for pasta manipulation by the head-fixed mouse. An ethogram of mice consuming pieces of spaghetti reveals that they eat in bite/chew bouts. A bout begins with pasta lifted to the mouth and then manipulated with hand movements into a preferred orientation for biting. Manipulation involves many hand release-reach movements, each with a similar structure. A hand is advanced from a digit closed and flexed (collect) position to a digit extended and open position (overgrasp) and then to a digit closed and flexed (grasp) position. Reach distance, hand shaping, and grasp patterns featuring precision grasps or whole hand grasps are related. To bite, mice display hand preference and asymmetric grasps; one hand (guide grasp) directs food into the mouth and the other stabilizes the pasta for biting. When chewing after biting, the hands hold the pasta in a symmetric resting position. Pasta-eating is organized and features structured hand movements and so lends itself to the neural investigation of skilled-movements.

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)0.001
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.258
Teacher spread0.238 · 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

Citations31
Published2017
Admission routes2
Has abstractyes

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