Spatio-temporal mapping of neritic benthic assemblages in sub-Arctic marine ecosystems
Bibliographic record
Abstract
Marine benthic assemblages tend to express spatio-temporal patterns, but mapping of benthic environments rarely addresses temporal aspects. To understand the spatial distribution of benthic assemblages, in situ samples (ground-truthing)—often using image and/or video data—can be combined with spatially continuous acoustic layers to predict full-coverage maps. However, most studies are built from a single ground-truthing event, making these maps a simple snapshot of the distribution of organisms without temporal variability. To address this limitation, we recorded seasonal benthic video of a sub-Arctic Bay in Newfoundland and Labrador, Canada, to describe the spatio-temporal changes that occurred in benthic communities across seasons. Biotic assemblage data were associated with environmental data layers derived from previously collected bathymetry and backscatter data to predict spatially continuous season-specific community maps. The predicted assemblages exhibited seasonal variability, reflecting differences in densities and locations of individual species. The most pronounced assemblage change was driven by the widespread disappearance of the most abundant species, the fuzzy sea cucumber Psolus cf. phantapus, in fall and winter. This species reduced in density from a peak of 2.955 m-2 in spring to <0.02 m-2 over fall and winter. The importance of the environmental data layers in explaining the difference between the assemblages varied across seasons despite these input layers being static. Without the incorporation of spatio-temporal dynamics into community studies, entire species can be missed or misrepresented. Capturing the natural fluctuations of sub-Arctic coastal ecosystems will be crucial in the early detection of perturbations caused by climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".