Global block kriging to estimate biomass from acoustic surveys for zooplankton in the western Aleutian Islands
Bibliographic record
Abstract
Because of patchy distributions and the expense of collecting and processing net samples, zooplankton population data are often characterized by broad confidence intervals, with little detailed information on vertical and horizontal distributions. Although acoustic techniques and optical plankton counters combined with nets can supply much more detailed information on plankton distributions, application of classical statistical procedures to such data may be effected by pseudoreplication from autocorrelation in closely spaced samples. We apply two-dimensional kriging for population mapping and global estimation. The technique utilizes a computational estimator of the global block kriging mean, which can be applied to large data sets common to hydroacoustic surveys. The techniques are outlined using examples from acoustic data taken in the western Aleutian Islands. This technique has two fundamental advantages: (i) it minimizes pseudoreplication by accounting for autocorrelation, permitting rigorous statistical inferences, and (ii) it provides an effective technique for visualizing the results. These advantages may make this a useful technique for identifying changes in the size and distribution of populations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".