Application and efficiency of beeswax casting and digital photogrammetry to study the morphology of a <i>Varanus</i> sp. foraging excavation
Bibliographic record
Abstract
This project assessed the value of wax casting, digital modelling and photogrammetry to model and measure a Varanus sp. digging. European honeybee wax proved to be a versatile and practical medium for casting the digging as it was robust to transportation, required no added liquid or onsite preparation and was easily melted and poured. Although solid, the resulting cast was not brittle and was not damaged during excavation, transportation and measurement. The volume of the cast was quickly and easily determined via weighing, fluid displacement and photogrammetry and the three volume measures varied by only 3%. The primary value of the cast was that it could be inverted and placed under a good light source for close examination, measurement and photography. The digital model of the digging was visually detailed and instantaneously showed measurements, including volume, surface area and maximum length, width and depth. Mechanical and photogrammetric linear measurements made on the surface of the cast and digital model were statistically identical. The principal cost of the digital modelling was the time required to learn to use the software and complete the modelling (42.5 hours). Therefore, this technique may only be feasible for studies of close range morphology or where parameters that can not otherwise be easily obtained, such as surface area and contours, are required.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".