Comparison of euphausiid population size estimates obtained using replicated acoustic surveys of coastal inlets and block average vs. geostatistical spatial interpolation methods
Bibliographic record
Abstract
Because of their intense patchiness, euphausiid spatial distributions and stock sizes are often assessed using echosounder surveys. However, statistical error bars appropriate for individual survey results are not well known. We quantified these by examining the statistical repeatability of acoustic estimates of total euphausiid biomass within two enclosed fjords adjoining the Strait of Georgia, British Columbia. Within each inlet, paired and replicated `mirror image' zig‐zag survey tracks provided sets of closely spaced backscatter profiles along the survey lines. Local stock density (biomass per unit area) was calculated by vertical integration across the euphausiid scattering layer. Total inlet population size was then estimated by horizontal interpolation and integration of the local measurements, both by block averaging and by geostatistical interpolation (kriging). By assuming no change in true population biomass over the short time interval separating replicate surveys, we could then estimate statistical precision by analysis‐of‐variance comparison among replicate survey grids. For the partial surveys (one or the other half of the mirror‐image paired grid) multiplicative error bars were about a factor of 1.5 for Jervis Inlet and 1.7 for Saanich Inlet. Use of the full surveys (both parts of the mirror‐image pair, roughly doubling the number of measurements in each estimate) reduced the error to about factor of 1.35 for Jervis but only to about 1.65 for Saanich. Statistical precision was similar for the block average vs. kriging interpolation and integration methods, however, kriging provided additional useful information about spatial pattern within the inlets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".