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Record W4223583864 · doi:10.31857/s0026898422020112

[Physical Basis of Functioning of Antifreeze Protein].

2022· article· en· W4223583864 on OpenAlexaff
Bogdan S. Melnik, Alexei V. Finkelstein

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsPROTO Manufacturing (Canada)
Fundersnot available
KeywordsAntifreeze proteinAntifreezeFreezing pointSupercoolingIce crystalsChemistryCryoprotectantIce formationBiophysicsMelting pointAstrobiologyCell biologyBiologyBiochemistryThermodynamicsPhysicsOrganic chemistryAtmospheric sciencesCryopreservationMeteorology

Abstract

fetched live from OpenAlex

Antifreeze proteins, expressed in cold-blooded organisms, prevent ice formation in their bodies, and thus help them to survive in extremely cold winter temperatures. However, the mechanism of action of these proteins is still not clear. In any case, it is not simply a decrease in the temperature of normal ice formation. In this work, investigating the ice-binding protein (a mutant form of the antifreeze protein cfAFP from the spruce budworm Choristoneura fumiferana, which overwinters in needles), we showed that this antifreeze protein does not at all lower the freezing point of water and, paradoxically, increases the melting point of ice. On the other hand, calculations based on the theory of crystallization show that at temperatures of 0° to -30°C ice can only appear on surfaces that contact water, but not in the body of water. These facts suggest a new perspective on the role of antifreeze proteins: their task is not (as it is commonly believed) to bind with nascent ice crystals already formed in the organism and stop their growth, but to bind to those surfaces, on which ice nuclei can appear, and thus completely inhibit the ice formation in supercooled water or biological fluid.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.019
GPT teacher head0.184
Teacher spread0.165 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations4
Published2022
Admission routes1
Has abstractyes

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