Comparison of recreational harvest estimates provided by onsite and offsite surveys: detecting bias and corroborating estimates
Bibliographic record
Abstract
We compare estimates of recreational harvest provided concurrently by two fundamentally different survey designs: an offsite panel survey and an onsite aerial-access survey. Aerial-access estimates for the five most commonly caught species were 2% to 50% lower than the panel survey estimates, with the greatest differences apparent for the least commonly caught species. Boosted regression tree modelling of spatially and temporally disaggregated harvest estimates identified a consistent pattern of temporal bias that explained much of the difference between the two sets of estimates. An analysis of web camera-based traffic data collected concurrently at key boat ramps confirmed that the selection of days for the aerial-access survey was biased towards lower effort days in three out of four temporal strata. Some evidence of under-reporting of zero catch trips by panelists was also identified by boosted regression tree modelling of catch per trip data. Nonetheless, the estimates provided by the two surveys were still remarkably similar given the range of potential biases that the two contrasting survey approaches were potentially subject to. Comparative studies such as this are rare, but they provide greater insights than introspective evaluations of single surveys and therefore greater certainty in the future.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".