Benthic Habitats and the Effects of Fishing
Bibliographic record
Abstract
<strong><em>Abstract. </em></strong>Soft corals (order Alcyonacea) are common on low-relief seabeds on the eastern Canadian shelf where they attach to empty shells and gravel. The effect of hydraulic clam dredging on the abundance of soft corals (specifically <em>Gersemia rubiformis</em>) was investigated on a sandy seabed at depths of 70–80 m on Banquereau, off eastern Canada. Visible soft corals were counted from video taken in two dredged and reference boxes before dredging; immediately after dredging; and 2 weeks, 1 year, and 2 years after dredging. Experimental dredging followed standard commercial practices. Dredge tracks covered approximately 53% and 68% of the surface area inside the two dredging boxes, and, although postdredging video surveys were conducted in heavily disturbed areas, no detectable effects of dredging on soft coral abundances were seen. However, the power of the analysis of variance was relatively low (0.6). Low dredge capture rates of soft corals also could partially explain the absence of an observed dredging effect on soft coral abundances. Dredge bycatch from the two dredging boxes showed that soft coral capture rates were variable and generally low (2% and 19%). In this sandy habitat, soft corals attach to empty shells at frequencies of 84% and higher. Most of the shell-attached soft corals were probably displaced from the dredge path through dredge-generated water turbulence. There are two spatial patterns of soft coral–shell associations on Banquereau. There is a low background density of soft corals (averaging 1–5 per 15 m2) with scattered patches (15–50 m in diameter) of dense accumulations of empty shells with attached epifauna including soft corals at higher abundances. It is expected that dredging through these dense patches, which are expected to be relatively hydrodynamically stable, would result in greater bycatch and damage to soft corals and disruption of these structurally complex seabed features.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".