An Integrative Method For Enhancing the Ecological Realism of Aquatic Artificial Habitat Designs Using 3D Scanning, Printing, Moulding and Casting
Bibliographic record
Abstract
Identifying features of biogenic (i.e., living) habitat that attract and retain organisms is a key pursuit in ecological habitat selection research. Here we present an integrative method for creating aquatic artificial habitat modules that allow the user to isolate and flexibly manipulate structural and compositional features of replicated biogenic habitats for a range of habitat selection study designs in aquatic environments: This method combines techniques from engineering (3D scanning and printing), paleontology, and visual art (moulding and casting) into a stream-lined work flow that is likely to perform on par with or better than other techniques widely used to create artificial replicas of biogenic habitats in terms of design accessibility (availability and cost of construction materials and equipment, and training requirements), scalability (durability, ease of deployment, and reproducibility), and the ecology of the artificial habitat module (degree to which structural and compositional features of the habitat elicit appropriate visual, chemosensory, and auditory cues, and impact of the structure on the surrounding environment). This method can be flexibly modified to answer a variety of questions regarding habitat selection cues, for a range of aquatic biogenic habitat types, and can be adapted for theoretical and applied contexts including cue studies and restoration planning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".