Experimental datasets on processed eggshell membrane powder for wound healing
Bibliographic record
Abstract
Eggshell (ES) and eggshell membrane (ESM) is a significant byproduct of the egg producing industry (Ahmed et al., 2019). Many studies have been undertaken to utilize ES waste for potential value added applications (Cordeiro and Hincke, 2011). Described here are the datasets from our evaluation of processed eggshell membrane powder (PEP) as a wound healing product using the mouse excisional wound splinting model (Ahmed et al., 2019). PEP biomaterial was characterized by proteomics using various extraction and solubilization strategies including moderate (lithium dodecyl sulphate (LDS) and urea/ammonium bicarbonate) and harsh conditions (3-mercaptopropionic acid (3-MPA) and NaOH/dimethylsulfoxide) in order to progressively overcome its stable, insoluble nature (Ahmed et al., 2019, Ahmed et al., 2017). Analysis of proteomic data allowed the relative abundance of the main PEP protein constituents to be determined. The efficacy of PEP for promotion of wound healing was assessed using the mouse excisional wound splinting model, and well-established semi-quantitative histological scoring. (More details about the PEP biomaterial characterization and its in vivo evaluation can be found in the related research article (Ahmed et al., 2019)).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".