Digital fisheries data in the Internet age: Emerging tools for research and monitoring using online data in recreational fisheries
Bibliographic record
Abstract
Abstract Recreational fisheries are diverse in scale, scope, and participation worldwide, constituting an important ecosystem service of marine and freshwater ecosystems. Management of these socio‐ecological systems is challenged by monitoring gaps, stemming from difficulties engaging with participants, biased sampling, and insufficient resources to conduct biological or social surveys of fish and human populations. In the Internet age, online data have great potential to make a meaningful contribution to recreational fisheries research, monitoring, and management. Recreational fishers in some countries increasingly use social and other digital media to share their experiences with followers, with most data freely available to web scrapers that compile databases of text (e.g. tweets, status updates, comments), photos, videos and other media that contain information about spatiotemporal activity, sentiments towards catches/experiences, targeted and bycatch species, effort levels, and more. Although the future of recreational fisheries research, monitoring and management will likely involve more digital scraping, uptake is only just beginning and there are several challenges including tool availability/accessibility, sampling biases, and making findings relevant and usable to practitioners. Despite these challenges, we envision fisheries managers will increasingly turn towards online sources of fisheries data to supplement conventional methods. We challenge scientists to work towards continued method development and validation of various digital fisheries data tools and emphasize how biases from the online behaviour of users may complicate interpretations of these data for fisheries management.
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 | 0.067 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".