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Record W1980283451 · doi:10.1080/00222895.2014.916652

The Potential Transformation of Our Species by Neural Enhancement

2015· article· en· W1980283451 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Motor Behavior · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsUniversity of VictoriaInternational Collaboration On Repair Discoveries
Fundersnot available
KeywordsNeuroprostheticsFunction (biology)Computer scienceHomo sapiensHuman enhancementNeurosciencePsychologyCognitive scienceCognitive psychologyArtificial intelligenceBiologyHistory

Abstract

fetched live from OpenAlex

Neural enhancement represents recovery of function that has been lost due to injury or disease pathology. Restoration of functional ability is the objective. For example, a neuroprosthetic to replace a forearm and hand lost to the ravages of war or industrial accident. However, the same basic constructs used for neural enhancement after injury could amplify abilities that are already in the natural normal range. That is, neural enhancement technologies to restore function and improve daily abilities for independent living could be used to improve so-called normal function to ultimate function. Approaching that functional level by use and integration of technology takes us toward the concept of a new species. This new subspecies--homo sapiens technologicus--is one that uses technology not just to assist but to change its own inherent biological function. The author uses examples from prosthetics and neuroprosthetics to address the issue of the limitations of constructs on the accepted range of human performance ability and aims to provide a cautionary view toward reflection on where our science may take the entire species.

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.

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 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 score0.002
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.051
GPT teacher head0.287
Teacher spread0.237 · 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