Development-based revision of bone tissue classification: the importance of semantics for science
Bibliographic record
Abstract
Research on the bone histology of extant and extinct animals has a long scientific history and an accurate description of microstructural tissues is the cornerstone of the field. Ideally, terminology needs to convey as much information as possible about the structural, developmental, and functional aspects of bone tissues corresponding to the up-to-date knowledge of the time. However, current terms are not always consistent with new observations and advances in the field of bone biology. We provide a brief overview of some ambiguities and their origins, and suggest a new approach of bone tissue classification and description that is congruent with our current understanding of bone as a living tissue, emphasizing its developmental aspects. This approach requires the introduction of a new term, namely ‘woven-parallel complex’, for describing a broad range of complex bone tissue types, including intramembranous and endochondral bones, and different types of primary as well as secondary bone tissues. We reconsider the classical concept of fibrolamellar complex, which we place within the new developmental approach. Finally, using non-archosaurian archosauromorphs as an example group, we demonstrate how the new approach can be utilized in an evolutionary context. The present study demonstrates the relevance and constant evolution of technical terminology along with the advances of the field of science.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".