Estimating abundance of spatially aggregated populations: comparing adaptive sampling with other survey designs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main goal in estimating population abundance is to maximize its accuracy and precision. This is difficult when the survey area is large and resources are limited. We implemented a feasible adaptive sampling survey applied to an aggregated population in a marine environment and compared its performance with five classical survey designs. Specifically, larval walleye pollock (Theragra chalcogramma) in the Gulf of Alaska was used as an example of a widespread aggregated population. The six sampling designs included (i) adaptive cluster, (ii) simple random, (iii) systematic, (iv) systematic cluster, (v) stratified systematic, and (vi) unequal probability. Of the five different adaptive estimators used for the adaptive cluster design, the modified Hansen–Hurwitz performed best overall. Of the six survey designs, the stratified systematic survey provided the best overall estimator, given there was accurate prior information on which to base the strata. If no prior information was available, a systematic survey was best. A systematic survey using a single random starting point with a simple random estimator performed as well as and sometimes better than a systematic cluster survey with two starting points (clusters). The adaptive cluster survey showed no advantages when compared with these two designs and furthermore presented substantial logistical challenges.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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 it