A global map to aid the identification and screening of critical habitat for marine industries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Marine industries face a number of risks that necessitate careful analysis prior to making decisions on the siting of operations and facilities. An important emerging regulatory framework on environmental sustainability for business operations is the International Finance Corporation’s Performance Standard 6 (IFC PS6). Within PS6, identification of biodiversity significance is articulated through the concept of “Critical Habitat”, a definition developed by the IFC and detailed through criteria aligned with those that support internationally accepted biodiversity designations. No publicly available tools have been developed in either the marine or terrestrial realm to assess the likelihood of sites or operations being located within PS6-defined Critical Habitat. This paper presents a starting point towards filling this gap in the form of a preliminary global map that classifies more than 13 million km2 of marine and coastal areas of importance for biodiversity (protected areas, Key Biodiversity Areas [KBA], sea turtle nesting sites, cold- and warm-water corals, seamounts, seagrass beds, mangroves, saltmarshes, hydrothermal vents and cold seeps) based on their overlap with Critical Habitat criteria, as defined by IFC. In total, 5798×103 km2 (1.6%) of the analysis area (global ocean plus coastal land strip) were classed as Likely Critical Habitat, and 7526×103 km2 (2.1%) as Potential Critical Habitat; the remainder (96.3%) were Unclassified. The latter was primarily due to the paucity of biodiversity data in marine areas beyond national jurisdiction and/or in deep waters, and the comparatively fewer protected areas and KBAs in these regions. Globally, protected areas constituted 65.9% of the combined Likely and Potential Critical Habitat extent, and KBAs 29.3%, not accounting for the overlap between these two features. Relative Critical Habitat extent in Exclusive Economic Zones varied dramatically between countries. This work is likely to be of particular use for industries operating in the marine and coastal realms as an early screening aid prior to in situ Critical Habitat assessment; to financial institutions making investment decisions; and to those wishing to implement good practice policies relevant to biodiversity management. Supplementary material (available online) includes other global datasets considered, documentation and justification of biodiversity feature classification, detail of IFC PS6 criteria/scenarios, and coverage calculations.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it