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Record W3205481266

Bryan Kolb: Pioneer in neuropsychology and role model for teaching, mentoring, and involving undergraduates in neuroscience research

2010· article· en· W3205481266 on OpenAlexaboutno aff
Gary Dunbar

Bibliographic record

VenueEurope PMC (PubMed Central) · 2010
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsnot available
Fundersnot available
KeywordsClinical neuropsychologyPsychologyNeuropsychologyMedical educationLibrary scienceNeuroscienceMedicineCognition
DOInot available

Abstract

fetched live from OpenAlex

Field Neurosciences Institute, 4677 Towne Centre, Saginaw, MI 48604. Bryan Kolb is a native of Calgary, Canada and is currently a Professor in the Neuroscience Department at the University of Lethbridge, where he has been since 1976. He received his PhD from Pennsylvania State University in 1973 and did postdoctoral work at the U of Western Ontario and the Montreal Neurological Institute. His recent work has focused on the development of the prefrontal cortex and how neurons of the cerebral cortex change in response to various developmental factors including hormones, experience, stress, drugs, neurotrophins, and injury, and how these changes are related to behavior. Bryan Kolb has published five books, including two textbooks with Ian Whishaw (Fundamentals of Human Neuropsychology, Sixth Edition; Introduction to Brain and Behavior, Third Edition), and over 300 articles and chapters. Kolb is a Fellow of the Royal Society of Canada and a Killam Fellow of the Canada Council. He is currently a member of the Canadian Institute for Advanced Research program in the Experience-Based Brain Development program. Bryan Kolb has won numerous teaching and research awards during his illustrious career, and his experience with mentoring undergraduate students provides an excellent model to emulate, for both young and seasoned faculty members who strive to provide their students with the best possible learning opportunities.

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)0.002
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.069
GPT teacher head0.330
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 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

Citations0
Published2010
Admission routes1
Has abstractyes

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