Alginate-based forensic blood substitutes mimicking whole blood clotting and drying properties in drip stains and pools
Bibliographic record
Abstract
) and time sweeps (3.5 % strain and 1 Hz for 2000 s). In single tests, blood pools formed from FBS2 appear to show drying characteristics closer to whole blood than FBS1. On non-porous tile, the FBS materials broadly replicated the speed and appearance of the early stages of drying of blood. It developed radial stress lines in the corona, but did not crack in the way blood does in the final stages of drying. It did not de-bond from the tile surface in the same way. In the limited tests performed, FBS1 appears to spread further than blood to form larger stains, whereas FBS2 may not. On cotton jersey, both FBSs wicked at a similar rate to blood. Both FBS1 and FBS2 dried lighter than blood, but with the same dark edge to the stain. Ultimately, we found that by changing the polymer and ionic crosslinker concentration, the physical fluid properties of the material can be manipulated to behave more like blood during stain formation. Thus, the current formulations of the FBS are suitable for the generation of blood stains for training and research, with FBS1 acting as an accurate mimetic of whole blood for drip stains, and FBS2 possessing the viscous behavior required to better mimic whole blood clotting in larger volume stains and pools. Further work with more replicates, applied volumes and substrates is needed.
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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 | 0.000 |
| 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) | 0.001 | 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".