MétaCan
Menu
Back to cohort
Record W4250781766 · doi:10.36866/pn.80.22

HIT to get fit: metabolic adaptations to low-volume high-intensity interval training

2010· article· en· W4250781766 on OpenAlexaff
Jonathan P. Little, Martin Gibala

Bibliographic record

VenuePhysiology News · 2010
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsMcMaster University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of Health
KeywordsHigh-intensity interval trainingIntensity (physics)Volume (thermodynamics)Training (meteorology)Adaptation (eye)Interval (graph theory)Computer scienceMathematicsPsychologyMedicineGeographyPhysiologyPhysicsMeteorologyNeuroscienceOpticsThermodynamics

Abstract

fetched live from OpenAlex

Among our scientific features, the Techniques series continues with the vital but not always so popular topic of statistics, specifically multiple regression (p.12).And I was fascinated by the article by Joshua Scallan and Virginia Huxley on those least studied of the body's vessels, the lymphatics (p.16).I have the subjective impression that the vessel interest 'hierarchy' runs arteries > veins > capillaries > lymphatics -but anyone who ever teaches about acute pulmonary oedema knows the importance of lymphatic physiology too.Finally, on p. 51 we say goodbye to a giant of 20th century physiology and biophysics, Professor Richard Keynes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.935
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.030
GPT teacher head0.283
Teacher spread0.253 · 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.

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
Published2010
Admission routes1
Has abstractyes

Explore more

Same venuePhysiology NewsSame topicCardiovascular and exercise physiologyFrench-language works237,207