Glass sponge reef soundscapes
Bibliographic record
Abstract
Many structured biogenic habitats are biodiversity hotspots and thus possess unique soundscapes largely driven by the biophony. To date, the vast majority of research has focused on shallow-water biogenic habitats such as coral or oyster reefs. Glass sponge reefs are a deep-water habitat analogous in many ways to shallow-water coral reefs. These reefs are built by three species of hexactinellid sponges which form complex 3-dimensional habitats that support diverse communities of animals. Many soniferous animals, including rockfish, are strongly associated with patches of live-sponge dominated habitat within the reef foot-print. Consequently, we hypothesized that glass sponge reefs and the communities they support would generate unique soundscapes. Beginning in September of 2016, we deployed a series of underwater acoustic recorders on sponge reefs throughout Canada’s Pacific continental shelf. Initial results show that recorders located on sponge reefs were significantly louder in the mid- and high-frequency bands (100–1000 Hz and 1–10 kHz, respectively). Additionally, many fish calls were detected in recordings from within sponge reefs, while few fish calls were observed at similar depths in off-reef habitats. We will discuss our understanding of the link between soundscapes and biodiversity in glass sponge reef habitats and the potential application of ecosystem-level monitoring.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".