Using 3D printing as a tool to study nesting behaviours of paracoprid dung beetles (Coleoptera: Scarabaeidae)
Bibliographic record
Abstract
Abstract The dung-burying activities of paracoprid dung beetles such as Onthophagus nuchicornis Linnaeus (Coleoptera: Scarabaeidae) are known to improve nutrient cycling, decrease greenhouse gas emissions, and reduce parasite transmission. These benefits are closely associated with the quantity of dung buried and the depth at which the nest is built; however, comparatively little research has focused on the role of underground nest architecture in underpinning ecosystem function. The use of three-dimensional (3D) printing has facilitated the use of innovative models, tools, and methods in recent ecological studies. Although past attempts have been made to construct paracoprid beetle observation chambers from wood, to our knowledge, 3D printing has not yet been used for this purpose. We designed a 3D-printed observation chamber that allowed us to view the placement and rate of brood-ball production. Initial trials of our design indicate that, with adjustment of the chamber interpane width, tunnelling and brood-ball activity can be monitored without limiting the activity of the captive beetles. Noninvasive observation of underground activity using 3D-printed observation chambers is cost and time effective, and it offers a number of practical advantages over traditional wooden designs. These improvements may facilitate observations and contribute to our understanding of ecosystem functions provided by paracoprid dung beetles.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".