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.
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 | 0.001 |
| 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) | 0.000 | 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".