Evaluating Batch Imaging as a Method for Non-Lethal Identification of Freshwater Fishes
Bibliographic record
Abstract
Freshwater fish community surveys are an important component of aquatic ecosystem management. However, the standard method for taxonomic identification currently used for these surveys, wherein fishes are manually identified in the field by a taxonomic expert, has several shortcomings. These include handling-related fish injury and mortality, the need for a fish-identification expert to be present during field sampling, and additional fish mortality due to physical voucher collection. These shortcomings may be overcome using new methods such as environmental DNA (eDNA) or image analyses. While eDNA can provide fish community data through metabarcoding, it is costly and provides little ecological information. A novel, image-based method for taxonomic identification (“batch-image identification”), which addresses the shortcomings of standard and eDNA methods, was tested in this study. Fishes were captured in the field and photographed in small groups (“batches”) within fish viewers for subsequent identification by taxonomic experts. Comparing taxonomist-based identifications from batch images to specimen-based identification, batch-image identification yielded an overall species-level correct-identification rate (CIR) of 49.7%, and an overall genus-level CIR of 61.2%. CIR increased with taxonomist expertise, reaching 83% when identification was performed by expert taxonomists. Batch-image identification data also produced rarefaction curves and fish-length measurements comparable to those obtained through standard methods. Potential methodological improvements to batch-image identification, including procedural adjustments and alternative identification methods, provide direction for the continued testing and improvement of this method.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".