DiversityScanner training and test insect images
Bibliographic record
Abstract
The dataset contains the images used in training, validating and testing the CNN used in the DiversityScanner robot. We subsequently used our own images with the detailed camera for the training image data set. We used 5 Malaise trap samples from 3 different locations in Germany near the small towns and villages of Rastatt, Kitzing and Framersbach and 145 3 from the Province of L’Aquila, Italy: Valle di Teve and Foresta Demaniale Chiarano-Sparvera. Thus, a mix of own images from different Malaise trap samples was used. The images for our target taxa were not equally distributed but reflected the abundances of each taxon in the Malaise trap samples. [11]. In total 4.325 color images in 15 classes were used for training, while 1.115 images were used for testing. Photos were taken with a Ximea MQ013CG-E2 camera with a telecentric Lensation TCST-10-40 lens with a magnification of 1x.
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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) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.026 |
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".