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Record W3112933767 · doi:10.1016/j.xpro.2020.100203

Visualizing Cellular Dynamics and Protein Localization in 3D Collagen

2020· article· en· W3112933767 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSTAR Protocols · 2020
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsDynamics (music)Cell biologyCellImmune systemEffectorComputer scienceMatrix (chemical analysis)BiologyComputational biologyChemistryImmunologyPhysicsGenetics

Abstract

fetched live from OpenAlex

Immune cells migrate and communicate through cell-to-cell interactions and cytokines to coordinate the specificity and timing of the immune response. While studying these events in cell culture are standard procedure, spatiotemporal dynamics of cell-to-cell interactions within three-dimensional (3D) environments are critical in generating appropriate effector functions. Here, we present a detailed protocol to study cells within an all-in-one 3D collagen matrix that is amenable to live-cell microscopy and immunohistochemistry. This approach facilitates analyses of dynamic cellular events in 3D settings. For complete details on the use and execution of this protocol, please refer to Koh et al. (2020).

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score0.440

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 studies0.0000.000
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 head0.040
GPT teacher head0.351
Teacher spread0.311 · 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