Bibliographic record
Abstract
This special issue of the Anatomical Record explores the presence and diversity of paranasal sinuses in distinct vertebrate groups. The following topics are addressed in particular: dinosaur physiology; development; physiology; adaptation; imaging; and primate systematics. A variety of approaches and techniques are used to examine and characterize the diversity of paranasal sinus pneumatization in a wide spectrum of vertebrates. These range from dissection to histology, from plain X-rays to computer tomography, from comparative anatomy to natural experimental settings, from mathematical computation to computer model simulation, and 2D to 3D reconstructions. The articles in this issue are a combination of literature review and new, hypothesis-driven anatomical research that highlights the complexities of paranasal sinus growth and development; ontogenetic and disease processes; physiology; paleontology; primate systematics; and human evolution. The issue incorporates a wide variety of vertebrates, encompassing a period of over 65 million years, in an effort to offer insight into the diversity of the paranasal sinus complexes through time and space, and thereby providing a greater understanding and appreciation of these special spaces within the cranium.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".