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Record W6945085908 · doi:10.24433/co.9801818.v1

Replication files for Neurons Learn by Predicting Future Activity

2021· other· en· W6945085908 on OpenAlexaff

Bibliographic record

VenueCode Ocean · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsSet (abstract data type)Code (set theory)Replication (statistics)Epoch (astronomy)Key (lock)Artificial neural network

Abstract

fetched live from OpenAlex

This capsule contains the code for "Neurons learn by predicting future activity" to reproduce the learning curves Figure 2-E (In this capsule, we set up maximum epoch = 3, because we would like to show that the network learns properly even with a small number of epochs ).

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)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.999
Threshold uncertainty score0.730

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.016
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0040.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.4890.235

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 head0.023
GPT teacher head0.287
Teacher spread0.264 · 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.

Study designNot applicable
DomainReproducibility
GenreDataset

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
Published2021
Admission routes1
Has abstractyes

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