A Tool Supporting the Extraction of Angling Effort Data from Remote Camera Images
Bibliographic record
Abstract
Abstract Estimating angling effort on more than a few lakes can be prohibitively expensive using creel surveys and often requires finer-scale angler distribution data than aerial surveys can provide. An alternate method uses remote cameras to capture images of lakes at hourly intervals over long time periods. Technicians then visually analyze the thousands of generated images for features of interest (e.g., angler counts and environmental conditions) and use those data to estimate angling effort. The problem is that the visual analysis step is time-consuming, expensive, and difficult to validate. Consequently, we elicited the strategies and best practices technicians used when analyzing images and identified bottlenecks. We then designed software, called Timelapse to better support image analysis. In use for several years, Timelapse has proven a cost-effective method of estimating angling effort in British Columbia's small lakes fisheries; it significantly eases a technician's workflow and doubles the number of images one can process per hour. La estimación del esfuerzo de pesca con anzuelo que se realiza en varios lagos mediante muestreos en puerto, puede llegar a tener costos prohibitivos y suele requerir una información más fina sobre la distribución del esfuerzo que la que proveen los muestreos aéreos. Un método alternativo se basa en la captura de imágenes de los lagos usando cámaras remotas, tomadas cada hora durante largos periodos. Posteriormente, los cientos de imágenes generadas son analizadas visualmente por los técnicos con el fin de detectar características de interés (v.g. conteo de pescadores y condiciones ambientales) y esta información se usa para estimar el esfuerzo de pesca. El problema es que el análisis visual consume mucho tiempo, es caro y difícil de validar. En consecuencia, en este trabajo se elucidan las estrategias y mejores prácticas que el personal técnico utiliza al analizar las imágenes e identificar cuellos de botella. Posteriormente se diseña un programa llamado Timelapse, como apoyo para el análisis de imágenes. Habiendo sido utilizado por varios años, Timelapse ha mostrado ser un método efectivo en cuanto a costos para estimar el esfuerzo en pesquerías de pequeños lagos en la Columbia Británica; alivia de forma importante el flujo de trabajo del personal técnico y duplica el número de imágenes que pueden procesarse en una hora.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".