State of Knowledge of Coastal and Marine Biodiversity of Indian Ocean Countries
Bibliographic record
Abstract
The Indian Ocean (IO) extends over 30% of the global ocean area and is rimmed by 36 littoral and 11 hinterland nations sustaining about 30% of the world's population. The landlocked character of the ocean along its northern boundary and the resultant seasonally reversing wind and sea surface circulation patterns are features unique to the IO. The IO also accounts for 30% of the global coral reef cover, 40,000 km² of mangroves,some of the world's largest estuaries, and 9 large marine ecosystems. Numerous expeditions and institutional efforts in the last two centuries have contributed greatly to our knowledge of coastal and marine biodiversity within the IO. The current inventory, as seen from the Ocean Biogeographic Information System, stands at 34,989 species, but the status of knowledge is not uniform among countries. Lack of human, institutional, and technical capabilities in some IO countries is the main cause for the heterogeneous level of growth in our understanding of the biodiversity of the IO. The gaps in knowledge extend to several smaller taxa and to large parts of the shelf and deep-sea ecosystems, including seamounts. Habitat loss, uncontrolled developmental activities in the coastal zone, over extraction of resources, and coastal pollution are serious constraints on maintenance of highly diverse biota, especially in countries like those of the IO, where environmental regulations are weak.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".