Adding the “where” to the “who and what”. Considering the seascape can help the study of biodiversity and ecosystem functioning.
Bibliographic record
Abstract
Whether we want to conserve, restore or enhance biodiversity, or use it to assess the environmental status of our coasts, the indissoluble link between biodiversity and ecosystem functioning is influenced by the spatial context of an ecosystem (the seascape). Using field-based research conducted in subarctic regions, we will show how the seascape can modulate species interactions which impair the habitat-forming functions of kelp. Specifically, bottom heterogeneity modulates top-down grazer control impacting the functioning of artificial structures as habitats for canopy-forming seaweeds in habitat compensation efforts. Unfortunately, coastal habitat maps from which seascape information could be obtained are often non-existant or are coarse in scale. We thus present how optical imagery-derived photomosaics can be used to map biological and geomorphological features over continuous and wide areas. Moreover, photomosaics can reveal patterns of local distribution of benthic species that can be useful when assessing biodiversity to evaluate the environmental status in coastal areas. Seascape context is thus a determining element which will improve our ability to maintain ecosystem functioning and services and inform coastal management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".