Characterizing sedimentary bedforms as habitat for fishes and invertebrates in the San Juan Archipelago, Washington, USA, and the Georgia Basin, British Columbia
Bibliographic record
Abstract
Based on multibeam bathymetry, multibeam backscatter datasets, Remotely Operated Vehicle (ROV) observations and sediment grab data collected in the San Juan Archipelago and Georgia Basin region, sedimentary bedforms including sediment waves, ripples and ripples overlying waves were identified and characterized as habitat for fishes and invertebrates according to the deep-water classification scheme for marine benthic habitats by Greene et al. (1999). Analysis of long term bathymetric survey data in a GIS suggests that sediment waves were dynamic and influenced by modern physical oceanographic processes. Direct observational data collected in the sediment wave fields revealed that sediment waves were poorly sorted and composed of both fine-grained sediment (sand) and coarse-grained sediment (cobbles, pebbles and coquina). Density, percent composition and distribution of fishes and invertebrates, specifically in San Juan Channel, were calculated. Pacific sand lance (Ammodytes hexapterus) had high percent composition values among the observed fishes in both the mixed and sand substrates. Spot prawns (Pandalus platyceros) dominated the gravel, mixed sediment and sand substrates. There were significant density differences for individuals in the families Hexagrammidae and Scorpaenidae, but there were no significant density differences for the remaining fish or invertebrates observed in both the sand wave and non-sand wave areas. Percent composition varied between gravel, mixed sediment, rock and sand substrates; however, based on Chi-squared analyses, there were significant differences detected among varying substrate types in both sand wave and non-sand wave transects which suggests that species occurrences are not independent of habitat types.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".