A cost-effective, open-source laboratory system for 3D photogrammetric analysis of corals
Bibliographic record
Abstract
Three-dimensional photogrammetry is a method used to measure 3D reconstructions created from 2D images. The precision of this method makes it widely used for studying ecosystem engineers such as corals. Although photogrammetry has been used to study both tropical and cold-water corals in situ, very few studies, with certain limitations including potential coral stress or low replicability, use photogrammetry to study corals in aquaria. For accurate 3D photogrammetric measurements of corals under laboratory conditions, we present the “Coruña 3D system” and the two prototypes that served as input for the development of the final setup. The “Coruña 3D system” is presented as a publicly accessible cost-effective setup used to obtain a complete set of images of a coral in an aquarium and create accurate 3D reconstructions. Using photogrammetry to study corals in aquaria enables the measurement and monitoring of different variables over both, short and long periods of time. The effectiveness of the system was assessed with a total of 120 3D reconstructions of cold-water corals. The system has resulted in a highly accurate tool, creating 3D reconstructions with a total scale error of 0.048 ± 0.079 mm (mean ± SD). Moreover, this open-source, cost-effective (<3000 €) system provided precise results overcoming the limitations of previous prototypes and mechanisms used in other studies. The adaptability of the "Coruña 3D system" according to the needs of the study makes it a versatile and useful tool to measure corals as well as other benthic marine species.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".