For IOC session 2: Monitoring progress of the Kunming-Montreal Global Biodiversity Framework in the deep sea
Bibliographic record
Abstract
A biodiversity crisis is unfolding in our world ocean with direct exploitation, climate change, pollution and alien invasive species as the main anthropogenic drivers. To respond to this crisis, the Kunming-Montreal Global Biodiversity Framework (GBF) set an ambitious plan to protect biodiversity on land and in the ocean through 4 Goals and 23 Targets to be met by 2050. To assess progress towards meeting those goals and targets, a monitoring framework of the GBF includes proposed headline, component and complementary indicators. However, many of these indicators have originated on terrestrial ecosystems and their applicability in the ocean has not been evaluated. For the deep ocean in particular, some of the headline indicators may not even be feasible to use. For example, Goal A aspires that the integrity, connectivity and resilience of all ecosystems are maintained, enhanced, or restored, substantially increasing the area of natural ecosystems by 2050, and will be assessed based on four indicators. Two of these, A.2 “the extent of natural ecosystems” and A.4, “the proportion of populations within species with an effective population size > 500” are not quantifiable in the deep ocean presently because of lack of data. However, indicator A.1, “Red list of ecosystems”, may be useable, at least for some deep-sea ecosystems. In this presentation, we will discuss the current feasibility of using the proposed indicators in the deep-sea context, using specific examples. We will address current gaps in scientific knowledge that may hinder the application of the monitoring framework of the GBF in the deep ocean and suggest ways forward. It is anticipated that the GBF will form the basis for the implementation of many elements of the BBNJ agreement and identifying the science needs that can support concurrently the implementation of both agreements to help advance the conservation of deep-ocean biodiversity.
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 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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.101 | 0.053 |
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".