ZOOMIE v 1.0 (Zooplankton Multiple Image Exclusion)
Bibliographic record
Abstract
5/25/2015 ZOOMIE v 1.0 (Zooplankton Multiple Image Exclusion) Moritz S. Schmid*, Cyril Aubry, Jordan Grigor, Louis Fortier Takuvik Joint International Laboratory, Laval University (Canada) – CNRS (France), UMI3376, Département de biologie et Québec-Océan, Université Laval, Québec, Québec G1V 0A6, Canada * Moritz.Schmid@takuvik.ulaval.ca 1. Introduction ZOOMIE is an image treatment tool developed to ensure optimal quality for images collected with the Lightframe On-sight Keyspecies Investigation (LOKI) System, an underwater zooplankton camera system. ZOOMIE does that by identifying cases where multiple pictures of the same specimen have been taken (hereafter referred to as double images), a phenomenon that frequently occurs when imaging plankton in a constrained volume during vertical deployments. The process of identifying double pictures can be carried out manually but is very time consuming. By applying ZOOMIE, the time needed to identify double images is substantially reduced. It is essential to account for double images when representative distributions of images are wanted ZOOMIE can automatically filter thousands of images based on previously extracted image parameters (e.g. area, mean grey pixel value, kurtosis; here extracted using the LOKI browser software (Isitec GmbH; http://www.isitec.de/start.htm)). The filtering is based on a set of rules that compares the image parameters of multiple images in order to detect double images and exclude them. The set of rules can be changed easily in the ZOOMIE scripts so that researchers can easily adapt the thresholds for finding double images necessary for their LOKI settings. After running the actual script to find double images, other scripts can be executed to automatically transfer images flagged for exclusion to a new folder. Finally, the results can be visualized on an internal homepage, using the actual images which are linked to the database. Here we can validate the outcome of the processing and we can manually adapt the outcome through dragging and dropping of images to verify if any images were wrongly allocated to a double image group. Although ZOOMIE was developed for LOKI images and the exclusion of double images, ZOOMIE could easily be adapted to handle other tasks requiring the handling and comparison of large numbers of images. _________________________________________________________________ This is an extract from the ZOOMIE introduction document available in the download. Thank you to Nicolas Garneau for initial set up of ZOOMIE (Université Laval). Recommended citation: Schmid MS, Aubry C, Grigor J and Fortier L (2015) ZOOMIE v1.0 (Zooplankton Multiple Image Exclusion) (Version 1.0) [Software]. Available from www.zenodo.org. doi:10.5281/zenodo.17928
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.171 | 0.076 |
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".