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Record W2889847253 · doi:10.1038/s41380-018-0244-9

Stress-inducible-stem cells: a new view on endocrine, metabolic and mental disease?

2018· editorial· en· W2889847253 on OpenAlexaff
Stefan R. Bornstein, Charlotte Steenblock, George P. Chrousos, Andrew V. Schally, Felix Beuschlein, Gregory Kline, Nils Krone, Júlio Licinio, Ma‐Li Wong, Enrico Ullmann, Gerard Ruiz‐Babot, Bernhard O. Boehm, Axel Behrens, Ana Brennand, Alice Santambrogio, Ilona Berger, Martin Werdermann, Rocı́o Sancho, Andreas Linkermann, Jacques W.M. Lenders, Cynthia L. Andoniadou

Bibliographic record

VenueMolecular Psychiatry · 2018
Typeeditorial
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsUniversity of Calgary
FundersTechnische Universität DresdenDeutsche ForschungsgemeinschaftKing's College LondonFrancis Crick Institute
KeywordsStem cellDiseaseEndocrine systemPsychologyNeurosciencePsychological stressStress (linguistics)MedicineBiologyClinical psychologyInternal medicineCell biologyHormonePhilosophy

Abstract

fetched live from OpenAlex

In general terms we all use the word “stress” to describe our discomfort in coping with challenges of daily life. This is mostly related to our subjective perceptions of workload and/or other unexpected physical or mental efforts we are exposed to. The term is derived from the concept of stress as a reaction to internal and external stimuli requiring acute or chronic adaptations, as introduced by Hans Selye in the second half of the last century [ 1 , 2 , 3 ].

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)0.004
metaresearch head score (Gemma)0.006
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.007
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0030.001
Research integrity0.0070.013
Insufficient payload (model declined to judge)0.0060.003

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 head0.012
GPT teacher head0.275
Teacher spread0.263 · 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
GenreEditorial

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

Citations28
Published2018
Admission routes1
Has abstractyes

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