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Record W2943674765 · doi:10.2110/palo.2019.010

REFINING THE CROFT PARALLEL GRINDER FOR ACETATE PEEL SERIAL SECTIONING AND VIRTUAL PALEONTOLOGY

2019· article· en· W2943674765 on OpenAlexaff
Y.H. Zhang, Colin D. Sproat, Renbin Zhan, Weimin Zhang, Xiaocong Luan, Bing Huang

Bibliographic record

VenuePalaios · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsUniversity of Saskatchewan
FundersState Key Laboratory of Palaeobiology and StratigraphyChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsRefining (metallurgy)GeologyPaleontologyMineralogyMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.220
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

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