Diagnostic tools for unbiased in situ target strength estimation
Bibliographic record
Abstract
In situ target strength (TS) is theoretically the optimal measure to scale echo-integration values to fish density. In practice, in situ TS is often biased. The number of fish per sample volume (Nv) has been used to set a threshold density to reduce the bias attributable to multiple targets. However, order of magnitude differences in the Nv threshold have been reported within the theoretical range 0 < Nv 1. To investigate the use and scale-dependence of the Nv index, with the objective of achieving unbiased estimates of in situ TS, redfish (Sebastes spp.) aggregations were measured in Newfoundland waters. When averaged over large horizontal distances (large scale), TS was biased upwards if Nv exceeded 0.04. However, TS could be estimated at higher densities without bias using smaller measurement scales. To deal with these scale-dependent variations, we develop diagnostic tools based on Nv and an echo-count index (Tv), which enable unbiased estimates of the Nv threshold and in situ TS.
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 | 0.007 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".