Fishery-independent gillnet study (FIGS) sampling protocol used for multi-species ecology study in Great Slave Lake, Northwest Territories, Canada
Bibliographic record
Abstract
Gillnets are among the most widely-used devices to capture fish for both scientific research and commercial purposes. The basic advantages of multimesh gillnets include facilitating the ability to catch a wide range of sizes and species, the flexibilities of installation in various combination of mesh-sized panels, and ease of operation. There are few detailed sampling protocols specifying the mesh size and gillnet dimension, sampling schedule, sample collection, and data quality assurance for fishery-independent scientific exploration using multimesh gillnets. The objective of this document is to outline a standard multimesh gillnet sampling protocol for a fishery-independent gillnet study (FIGS), which aims to quantitatively investigate species richness, species-specific abundance and biomass indices, analogous to catch per unit effort (CPUEs), population structure, and multi-species community dynamics in Great Slave Lake (GSL), a large northern boreal lake situated in the Northwest Territories, Canada. To conduct FIGS, an index gillnet comprised ten different mesh-sized panels, ranging between 13–140 mm (½–5½”) knot-to-knot stretched, which followed a geometric progression mesh size factor of r = 1.31. The height of the panels was 3.7 m (12’) and 1.8 m (6’) for pelagic and benthic sets, respectively. The lengths of the panels varied in groups of mesh sizes in order to reduce the catch/mortality of small-sized fishes in small mesh size panels: 11 m (36’) for smaller mesh size panels (13–38 mm; ½–1½”) and 22 m (72’) for larger mesh-size panels (51–140 mm; 2–5½”). Associated with proportions of area-specific grid numbers and depth-specific strata, the selection of sampling grid, type and number of gillnet, and order of deployment was made following a depth stratified random sampling strategy. Regardless, the multimesh gillnet design used for FIGS can be applied as a standard tool to monitor fish population status, fish community association, capture efficiency, and to potentially support quantitative fisheries stock assessment in large lakes. By applying this protocol to routine monitoring and assessment, it provides an important step towards delivering reliable, robust, and representative estimates of fisheries production and improves the interpretability and reliability of biological reference points into integrated fisheries management plans (IFMP), fish stock provisions, and ecosystem based fisheries management (EBFM) in particular for Arctic great lakes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".