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Record W2483986085 · doi:10.1017/cjn.2016.272

Commemorating John F. MacDonald and the Art of Being a Mentor

2016· review· en· W2483986085 on OpenAlexafffundvenue
Michael Jackson, Vânia F. Prado, Marco A. M. Prado

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2016
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsWestern UniversityUniversity of Manitoba
FundersCanadian Institutes of Health ResearchWeston Brain InstituteNatural Sciences and Engineering Research Council of CanadaFondation Brain Canada
KeywordsFriendshipNeurosciencePsychoanalysisArt historyEnvironmental ethicsHistoryPsychologyPhilosophy

Abstract

fetched live from OpenAlex

John F. MacDonald was a close friend and mentor whose life was ended far too soon on April 22, 2014. To those who knew him, John was an endearing blend of fiery Scotsman, compassionate socialist, dedicated family man, and tireless investigator. Those close to him valued his loyalty and friendship, relished his biting wit, and puzzled at his self-deprecating manner. His career spanned a remarkable period of discovery from the early identification of excitatory amino acid, to the molecular cloning and characterization of glutamate receptors and the elucidation of mechanisms responsible for regulating their function. A true pioneer in each of these areas, John's research has had a lasting impact on our understanding of excitatory synaptic transmission and its plasticity. Our intent in commemorating John's work is to focus on some notable discoveries that highlight the impact and innovative aspects of John's work. In doing so, we also wish to highlight just how greatly our understanding of the glutamate transmitter systems has advanced since the late 1970s, when John first launched his independent neuroscience career.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.003

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 head0.104
GPT teacher head0.370
Teacher spread0.266 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2016
Admission routes3
Has abstractyes

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