Senckenberg Policy Brief - Diving through the darkness; Species information is vital for effective marine conservation
Bibliographic record
Abstract
World leaders and representatives of 196 contracting states are joining the 15th Conference of the Parties (COP15) to the Convention on Biological Diversity (CBD) in Montréal to discuss strategies to stem global biodiversity loss. Worldwide, one million species are currently threatened with extinction from increasing anthropogenic impacts. Recently discovered species are predicted to have unusually high risks of extinction; many of these occur in biodiversity-rich marine environments such as coral reefs and the deep sea. Conservation of deep-sea species found in “areas beyond national jurisdiction” is particularly Above: Diversity brought to light during a single deep-sea sampling campaign - the recent Aleut-Bio expedition to the Aleutian Trench on board RV Sonne (species not shown to scale). challenging because we know very little about them, and there is not yet an international framework to guide the implementation of conservation measures. Deep-sea ecosystems form the largest realm on Earth that harbour a vast number of species, but remain least explored. The deep sea and its unique and rich biodiversity play a key role in ecosystem services, such as food supply or regulating global climate by absorbing heat and sequestrating carbon dioxide from the atmosphere. However, scientists estimate that about 90% of species in the oceans are not yet discovered or have no name. We urge decision makers to further support species discovery, including deep-sea exploration activities. Protection of undescribed biodiversity must work alongside efforts to increase species information to ensure effective marine conservation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.036 | 0.002 |
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; both teacher heads agree on what is shown here.
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".