Editorial: Deep-sea sponge ecosystems: Knowledge-based approach towards sustainable management and conservation
Bibliographic record
Abstract
Deep-sea sponge ecosystems: Knowledge-based approach towards sustainable management and conservation Sponge-dominated communities form a variety of habitatssponge grounds, aggregations, gardens, and reefsthat are widespread throughout the world's oceans (Maldonado et al., 2015).They are especially prevalent in upper bathyal areas within national jurisdictions (e.g.continental shelves and slopes, Klitgaard and Tendal, 2004), but also occur on seamounts, mid-ocean ridges, and canyons in areas beyond national jurisdictions (e.g.Murillo et al., 2012;Xavier et al., 2015).On account of their uniqueness, functional significance (Pham et al., 2019) and vulnerability to direct and indirect impacts from established (e.g.bottom fishing; Murillo et al., 2016) and emerging (e.g.deep-sea mining) anthropogenic activities, these habitats were listed by the Oslo-Paris Convention for the Protection of the Marine Environment of the North-East Atlantic as threatened and/or endangered (OSPAR Commission, 2008), and many are classified as Vulnerable Marine Ecosystems (VMEs -FAO, 2009).Ensuring these habitats' long-term sustainability therefore requires a sound scientific knowledge base to be brought to the highest levels of the political and conservation agendas.This Research Topic invited the international scientific community to address major knowledge gaps on deep-sea sponge habitats, with a particular focus on those in the North Atlantic and Arctic Ocean, resulting from the H2020 SponGES project 1 , but including also studies performed in the Mediterranean Sea and the Pacific Ocean.Collectively, these studies enabled:1 www.deepseasponges.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.036 | 0.026 |
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".