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Record W2885558946 · doi:10.1101/cshperspect.a032805

Careers in Core Facility Management

2018· review· en· W2885558946 on OpenAlexaff
Claire M. Brown

Bibliographic record

VenueCold Spring Harbor Perspectives in Biology · 2018
Typereview
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsSophisticationPaceCore (optical fiber)Facility managementHuman multitaskingCore competencyField (mathematics)Public relationsEngineering ethicsComputer scienceBusinessEngineeringPolitical scienceMarketingPsychologySociology

Abstract

fetched live from OpenAlex

The need for centralized shared core facilities and highly qualified core facility staff is becoming increasingly important in universities, research institutes, and commercial laboratories. With the continued advancement and sophistication of scientific equipment typically comes a larger price tag than can be handled by individual research laboratories. Moreover, the ever-increasing need for researchers to think and act in cross-disciplinary environments, coupled with the increasing sophistication of both the instrumentation and associated technologies, prevents most researchers from becoming "experts" in all areas.At all levels, core facility positions involve a love of technology, working with people, working on many diverse scientific questions, and days full of multitasking. Entry-level positions include basic and advanced technicians that require a BSc or MSc degree and some experience in the field. Midlevel management positions require experience in the field and an MSc or PhD degree. Management experience is a plus but not always required. Scientific directorship positions require a PhD and a keen interest in the technologies that are typically applied in the director's research program. Associate deans of core resources are often former core managers or scientific directors with a vision for the core and who are strong administrators.A career as a core facility staff member can be very rewarding. Successful managers and directors must be able to multitask, reassess priorities, and be adept at using logical reasoning to identify and solve issues as they arise. These positions will continue to be available over the long term with the increasing complexity and continued fast pace of technology development.

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)0.014
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0080.004
Scholarly communication0.0090.006
Open science0.0020.010
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0490.020

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 head0.244
GPT teacher head0.453
Teacher spread0.209 · 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
DomainIncentives
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

Citations10
Published2018
Admission routes1
Has abstractyes

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