Spatial and Temporal Variation in Efficiency of the Moore Egg Collector
Bibliographic record
Abstract
Abstract The Moore egg collector (MEC) was developed for quantitative and nondestructive capture of semibuoyant fish eggs. Previous studies have indicated that capture efficiency of the MEC was low and the use of one device did not adequately represent the spatial distribution within the water column of egg surrogates (gellan beads) of pelagic broadcast-spawning cyprinids. The objective of this study was to assess whether use of multiple MECs showed differences in spatial and temporal distribution of bead catches. Capture efficiency of three MECs was tested at four 500-m sites on the South Canadian River, a Great Plains river in Oklahoma. For each trial, approximately 100,000 beads were released and mean capture efficiency was 0.47–2.16%. Kolmogorov–Smirnov tests indicated the spatial distributions of bead catches were different among multiple MECs at three of four sites. Temporal variability in timing of peak catches of gellan beads was also evident between MECs. We concluded that the use of multiple MECs is necessary to properly sample eggs of pelagic broadcast-spawning cyprinids. Received May 2, 2013; accepted July 4, 2013
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".