Use of laser‐scanning confocal microscopy in the detection of diagenesis in bone
Bibliographic record
Abstract
This research demonstrates the value of laser scanning confocal microscopy (LSCM) as a research tool in osteological studies, and diagenetic studies in particular. LSCM combines properties of light and scanning electron microscopy using laser light to excite fluorophores throughout the z-axis, developing a 3-D image. Using differential staining and selecting for specific wavelengths of light, one can image targeted materials. This research is divided into two parts: visualizing bone structures such as proteins and their decompositional products and visualizing diagenesis. Part one of this study utilized pig bones as a means of testing the overall ability of LSCM to fluoresce bone. Twenty-three samples were imaged, including 13 samples from a decompositional study conducted 5 years previous, and 10 "fresh" samples collected from a commercial butcher. This part of the study determined that protein and organic components of the bone could be fluoresced and diagenetic alteration could be imaged. The second part of the study used human samples as a means of imaging and mapping diagenetic alterations. The second part of the study used 13 samples, including 4 clinical, 7 ancient, and 2 modern controls. The pig study used Basic Fuchsin and SlowFade Gold stains, while the human study used toluidine blue. Images were also taken with unstained elements. The results of the non-human study found that a fresh bone fluoresced differently than that of a 5-year subset, while the results of the human study confirmed these findings and determined that the bone diagenesis can be mapped using LSCM.
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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 | 0.001 | 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".