Angler Effort Estimates from Instantaneous Aerial Counts: Use of High-Frequency Time-Lapse Camera Data to Inform Model-Based Estimators
Bibliographic record
Abstract
Abstract Due to the logistics of monitoring remote and diffuse fisheries, typically only a small portion of the annual fishing effort can be observed, and a random sample is often impractical. We used a large data set (over 250,000 observations) from time-lapse cameras placed at 53 lakes across the province of British Columbia, Canada, to better understand the relative influence of different temporal strata (time of day, day type, and month) on the relative number of fishing boats observed on lakes. The high temporal frequency data available from cameras were used to evaluate alternative effort monitoring and estimation strategies for aerial surveys that are commonly used in recreational fisheries. We tested the predictive performance of simple mean count expansion factors versus generalized linear mixed models (GLMMs) under random and stratified (targeting high-effort strata) sampling regimes. Out-of-sample cross validation showed that the current sampling protocol and estimation method (mean count expansion) used for aerial surveys in British Columbia have low predictive power and are biased. A stratified sampling design combined with the GLMM estimator was an efficient strategy that could easily be applied to effort monitoring programs using aircraft. Moreover, the GLMM approach is much more flexible to interpret data obtained from nonstandard sampling regimes and can estimate angler effort over any temporal scale. We outline several summary statistics on accuracy, precision, and bias of estimates at different sample sizes so that managers can evaluate expected precision versus sampling effort in their management jurisdictions. Furthermore, we suggest that our general approach of using camera data to parameterize predictive models applied to data from other low-frequency effort monitoring methods (such as aircraft) can easily be replicated elsewhere.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".