A model for the time dependent three‐dimensional thermal distribution within iceballs surrounding multiple cryoprobes
Bibliographic record
Abstract
A time dependent three-dimensional finite difference model of iceball formation about multiple cryoprobes has been developed and compared to experimental data. Realistic three-dimensional probe geometry is specified and the number of cryoprobes, the cryoprobe cooling rates, and the locations of the probes are arbitrary inputs by the user. The simulation accounts for observed longitudinal thermal gradients along the cryoprobe tips. Thermal histories for several points around commercially available cryoprobes have been predicted within experimental error for one, three, and five probe configurations. The simulation can be used to generate isotherms within the iceball at arbitrary times. Volumes enclosed by the iceball and any isotherms may also be computed to give the ablative ratio, a measure of the iceball's killing efficiency. This ratio was calculated as the volume enclosed by a critical isotherm divided by the total volume of the iceball for assumed critical temperatures of -20 and -40 degrees C. The ablative ratio for a single probe is a continuously decreasing function of time but when multiple probe configurations are used the ablative ratio increases to a maximum and then essentially plateaus. Maximum values of 0.44 and 0.55 were observed for three and five probe configurations, respectively, with an assumed critical temperature of -20 degrees C. Assuming a critical temperature of -40 degrees C, maximum ablative ratios of 0.21 and 0.3 for three and five probe configurations, respectively, were observed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".