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Record W2026635876 · doi:10.1155/2014/976895

Bacterial Ice Crystal Controlling Proteins

2014· review· en· W2026635876 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.

Bibliographic record

VenueScientifica · 2014
Typereview
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAntifreeze proteinIce nucleusIce crystalsPsychrophileNucleationAntifreezeChemistrySupercoolingBiophysicsCell biologyBiologyBiochemistryPhysicsThermodynamicsMeteorologyOrganic chemistry

Abstract

fetched live from OpenAlex

Across the world, many ice active bacteria utilize ice crystal controlling proteins for aid in freezing tolerance at subzero temperatures. Ice crystal controlling proteins include both antifreeze and ice nucleation proteins. Antifreeze proteins minimize freezing damage by inhibiting growth of large ice crystals, while ice nucleation proteins induce formation of embryonic ice crystals. Although both protein classes have differing functions, these proteins use the same ice binding mechanisms. Rather than direct binding, it is probable that these protein classes create an ice surface prior to ice crystal surface adsorption. Function is differentiated by molecular size of the protein. This paper reviews the similar and different aspects of bacterial antifreeze and ice nucleation proteins, the role of these proteins in freezing tolerance, prevalence of these proteins in psychrophiles, and current mechanisms of protein-ice interactions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.005

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 head0.039
GPT teacher head0.270
Teacher spread0.231 · 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