Standard Methods for Sampling Freshwater Fishes: Opportunities for International Collaboration
Bibliographic record
Abstract
Abstract With publication of Standard Methods for Sampling North American Freshwater Fishes in 2009, the American Fisheries Society (AFS) recommended standard procedures for North America. To explore interest in standardizing at intercontinental scales, a symposium attended by international specialists in freshwater fish sampling was convened at the 145th Annual AFS Meeting in Portland, Oregon, in August 2015. Participants represented all continents except Australia and Antarctica and were employed by state and federal agencies, universities, nongovernmental organizations, and consulting businesses. Currently, standardization is practiced mostly in North America and Europe. Participants described how standardization has been important for management of long-term data sets, promoting fundamental scientific understanding, and assessing efficacy of large spatial scale management strategies. Academics indicated that standardization has been useful in fisheries education because time previously used to teach how sampling methods are developed is now more devoted to diagnosis and treatment of problem fish communities. Researchers reported that standardization allowed increased sample size for method validation and calibration. Group consensus was to retain continental standards where they currently exist but to further explore international and intercontinental standardization, specifically identifying where synergies and bridges exist, and identify means to collaborate with scientists where standardization is limited but interest and need occur.
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 | 0.447 | 0.244 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".