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Record W7030022037

Modelling mitochondrial complex IV bioenergetics

2016· dissertation· en· W7030022037 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMspace (University of Manitoba) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicEducation and Art Integration
Canadian institutionsnot available
Fundersnot available
KeywordsBioenergeticsMitochondrionReagentOxygenOxidative phosphorylationAntimycin AFunction (biology)
DOInot available

Abstract

fetched live from OpenAlex

A computational model for mitochondrial function has been developed from oxygen concentration data measured in the Oroboros Oxygraph-2k and oxygen consumption rates measured in the Seahorse XF24 Analyzer. Measurements were acquired using embryonic-cultured cortical neurons and isolated mitochondria from CD1 mice. Based on the biological mechanism of mitochondrial activity, a computational model was developed using biochemical kinetic modelling. To modulate mitochondrial activity, dysfunctions were introduced by injecting the inhibiting reagents oligomycin, rotenone, and antimycin A, and the uncoupling reagent carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP) during measurements. To incorporate these changes, model equations were adapted and globally calibrated to experimental data using the genetic algorithm developed by Jason Fiege of the University of Manitoba by fitting oxygen concentration data. The model was coded in MATLAB R2014a along with the development of a graphical user interface for simulating mitochondrial bioenergetics in silico.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.996

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 studies0.0010.000
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 head0.045
GPT teacher head0.274
Teacher spread0.228 · 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