MétaCan
Menu
Back to cohort

Engineered Compounds to Control Ice Nucleation and Recrystallization

2023· review· en· W4367186715 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

VenueAnnual Review of Biomedical Engineering · 2023
Typereview
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsCanadian Blood ServicesUniversity of OttawaUniversity of Alberta
FundersCumming School of Medicine, University of Calgary
KeywordsRecrystallization (geology)NucleationCryobiologyIce nucleusIce formationNanotechnologyBiologyChemistryMaterials scienceCell biologyCryopreservationGeologyPaleontology

Abstract

fetched live from OpenAlex

One of the greatest concerns in the subzero storage of cells, tissues, and organs is the ability to control the nucleation or recrystallization of ice. In nature, evidence of these processes, which aid in sustaining internal temperatures below the physiologic freezing point for extended periods of time, is apparent in freeze-avoidant and freeze-tolerant organisms. After decades of studying these proteins, we now have easily accessible compounds and materials capable of recapitulating the mechanisms seen in nature for biopreser-vation applications. The output from this burgeoning area of research can interact synergistically with other novel developments in the field of cryobiology, making it an opportune time for a review on this topic.

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)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.934
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.290
Teacher spread0.269 · 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