Three-dimensional simulation of ice growth in the presence of antifreeze proteins
Bibliographic record
Abstract
A Monte Carlo computational method for simulating the growth of entire ice crystals from the liquid phase has been developed specifically to study the inhibition of ice-crystal growth by antifreeze proteins (AFPs). AFPs are found in the fluids of certain organisms that inhabit freezing environments and constrain ice-crystal growth by adsorbtion to the ice surface, but their inhibition mechanism is still poorly understood. Thus, it was of interest to incorporate these molecules into the dynamic ice simulations to examine the inhibition phenomenon on a whole-crystal basis. We have undertaken simulations with AFPs from two different organisms that differ in activity; the insect AFP has up to 100 times the activity of the fish AFP on a molar basis. Simulations involving insect and fish AFPs have achieved ice-growth inhibition at simulation temperatures within reported activity ranges for both fish and insect AFPs, accompanied by resulting ice morphologies similar to those observed experimentally. These results, as well as other studies on ice-etching patterns and ice burst growth at temperatures below known AFP ice-growth inhibition abilities suggest that AFP activity is dominated by the AFP ice-binding position rather than AFP ice-binding strength. PACS No.: 07.05T
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".