A two-dimensional geostatistic method to simulate the precision of abundance estimates
Bibliographic record
Abstract
In this paper, we outline a geostatistic method to simulate the relative precision (coefficient of variation, CV) of total abundance estimates of one species in a predetermined, stratified area when it is appropriate to treat the observations within each stratum as realizations of a second-order homogenous and ergodic random process. To model the spatial correlations, a variogram is fitted to normal-transformed values of the original observations. Based on the variogram and its corresponding covariance matrix, extensive simulations on a fine grid that includes the sample locations provide random realizations of the process. The normal values are back-transformed to original observation space by nonparametric reversed bootstrap, as well as by a parametric Weibull approach. The method is applied to a total of 1069 shrimp (Pandalus borealis) abundance observations from 11 annual surveys in the Barents Sea (1992–2002) where a 20 nautical mile sampling grid has been applied. On average, the CV was estimated to be 6.4% for the applied regular grid when the simulations were conditional on the observations, compared with 8.1% when the sampling locations within each of the six strata were random.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".