Passive Drip Stain Formation Dynamics of Blood onto Hard Surfaces and Comparison with Simple Fluids for Blood Substitute Development and Assessment<sup>,</sup>
Bibliographic record
Abstract
Abstract The spreading dynamics of blood dripping onto hard surfaces is compared to two spreading models. Samples of human blood, porcine blood, and Millipore ® water were dripped onto cardboard, foamcore, and glass surfaces in low velocity passive drip simulations. Final stain diameter, the total number of spines and scallops, and angle of impact were measured and analyzed. Spreading is best predicted by applying the concept of effective viscosity to the Scheller and Bousfield ( R 2 = 0.91) and Roisman ( R 2 = 0.89) spreading models. In the tested conditions, blood spreads with Newtonian tendencies; however, has quantifiable differences in stain appearance to Newtonian fluids like water. This is encouraging for the development of water‐based fluids as synthetic blood substitutes ( SBS s). The work presents an assessment platform to quantify and score the performance of simple water‐based fluids using final stain diameter (6 points) and number of spines and scallops (6 points) at six dripping heights between 20 and 120 cm. The angle of impact of a stain alone is not a sensitive measure of SBS performance, but stain formation scores the SBS 's performance with another 1 point. Together these features generate a quantitative relative ranking system, of a maximum possible 13 points, that can be used to support the use of a particular fluid for the creation of a drip stain. The performance of twenty simple fluids in the simulated dripping assessment test is described.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".