Multibeam sonar (DIDSON) assessment of American shad (<i>Alosa sapidissima</i>) approaching a hydroelectric dam
Bibliographic record
Abstract
We investigated the fish community approaching the Veazie Dam on the Penobscot River, Maine, prior to implementation of a major dam removal and river restoration project. Multibeam sonar (dual-frequency identification sonar, DIDSON) surveys were conducted continuously at the fishway entrance from May to July in 2011. A 5% subsample of DIDSON data contained 43 793 fish targets, the majority of which were of Excellent (15.7%) or Good (73.01%) observation quality. Excellent quality DIDSON targets (n = 6876) were apportioned by species using a Bayesian mixture model based on four known fork length distributions (river herring (alewife, Alosa psuedoharengus, and blueback herring, Alosa aestivalis), American shad, Alosa sapidissima) and two size classes (one sea-winter and multi-sea-winter) of Atlantic salmon (Salmo salar). 76.2% of targets were assigned to the American shad distribution; Atlantic salmon accounted for 15.64%, and river herring 8.16% of observed targets. Shad-sized (99.0%) and salmon-sized (99.3%) targets approached the fishway almost exclusively during the day, whereas river herring-sized targets were observed both during the day (51.1%) and at night (48.9%). This approach demonstrates how multibeam sonar imaging can be used to evaluate community composition and species-specific movement patterns in systems where there is little overlap in the length distributions of target species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".