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.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".