REFINING THE CROFT PARALLEL GRINDER FOR ACETATE PEEL SERIAL SECTIONING AND VIRTUAL PALEONTOLOGY
Bibliographic record
Abstract
ABSTRACT The design of the classic Croft parallel grinder from 1950 was updated to improve usability and better facilitate the imaging and reconstruction of specimens. The grinder is capable of producing thin slices to 10–30 μm. Specimens as large as 37 mm in length/width and 50 mm in height can be accommodated. Acetate peels can be used in conjunction with the grinder to produce a permanent record of shell interiors, similar to a low-tech and inexpensive version of a CT scan. To simplify the registration process, a new alignment socket was designed to restrict the position and orientation of the specimen when it is photographed or scanned to aid in aligning the resulting series of digital images for three-dimensional reconstruction. To test the updated grinder, a barnacle and two oyster shells were serially ground to demonstrate its effectiveness in virtual paleontology applications. This design allows for lower cost virtual paleontology studies in comparison to high-tech methods such as micro-CT scanning, and even provides some advantages in terms of the level of detail preserved. Instructions for manufacture and assembly of the redesigned grinder are accessible as online supplemental material, and are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License, ensuring that they are freely distributable and modifiable in the future.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".