The use of Dual-frequency IDentification SONar (DIDSON) to document white sturgeon activity in the Columbia River, Canada
Bibliographic record
Abstract
The feasibility of using Dual-frequency IDentification SONar (DIDSON) for monitoring white sturgeon (Acipenser transmontanus) presence and activity was tested near a known spawning area in the Columbia River, British Columbia, Canada. A fixed-station DIDSON system was deployed near the river bank adjacent to the spawning site in each 3 years (2007–2009). Fixed-station data were collected at this site in July and August each year, with an additional fixed-station site established in 2009 approximately 1.6 km upstream. A total of 267, 64, and 210 observations of sturgeon were documented based on fixed-station DIDSON sampling in 2007, 2008, and 2009, respectively. Sturgeon detections within the sample area (standardized by time and day) generally increased during late evening/early morning hours but did not appear to be related to flows. The DIDSON provided estimates of white sturgeon total lengths consistent with known length distributions for this population. Most sturgeon were detected at least 10 m away from the shoreline. These results demonstrate the feasibility of using fixed-station DIDSON for remotely monitoring white sturgeon in areas of known use. Observational data from this study also provided information on general sturgeon behaviour that is often difficult to assess with more conventional sampling methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".