Bibliographic record
Abstract
Icicles are a common sight during winter, hanging from rooftops and otherstructures from which water drips. The long slender shape of an icicle develops through a highly non-equilibrium process in which water flows down its surface, partially freezing along the way. This is an example of ``wet ice growth.'' Many icicles exhibit rippled pattern along their length with a near universal9~mm wavelength. While measurements of icicle ripples date back to at least 1933, the mechanism through which they form has eluded physicists. The existence and amplitude of ripples was previously shown to depend on the presence of impurities in the source water, but no existing model for icicle ripples includes impurities. The problem of how icicles form in the presence of impurities is the focus of this thesis. A pre-existing model for the rippling instability was extended to includephysical effects of impurities. A linear stability analysis performed on this model showed that it did not predict ripples. The stability of the model is not sensitive to concentration, so such a model will likely never agree with experiment. A set of 120 icicles were grown using various chemical species as the impurity.The changes in morphology from impurities were only affected by the molar concentration, showing that icicle ripples only depend on the number of dissolved molecules. One of the impurities used was a dye that only fluoresces when dissolved inwater. The liquid flowing down the surface of icicles was observed directly using this dye. Contrary to previous models, we find that the ice is incompletely and intermittently wetted by the liquid phase, and the concentration of impurities modifies the wetting properties of ice. The location of impurities trapped inside of icicles was also observed. All ofthe impurities were found inside small spherical inclusions. The inclusions are organized into chevron patterns aligned with the peaks of ripples, with a layered substructure, suggestive of cyclic wetting and freezing. These observations must inform any successful model of an impurity-drivenrippling instability. Our results have general implications for the evolution of many gravity-driven wet ice growth processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".