Alginate-based forensic blood substitutes mimicking whole blood clotting and drying properties in drip stains and pools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.001 |
| 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 it