Tiger sharks support the characterization of the world's largest seagrass ecosystem
Bibliographic record
Abstract
Seagrass conservation is critical for mitigating climate change due to the large stocks of carbon they sequester in the seafloor. However, effective conservation and its potential to provide nature-based solutions to climate change is hindered by major uncertainties regarding seagrass extent and distribution. Here, we describe the characterization of the world's largest seagrass ecosystem, located in The Bahamas. We integrate existing spatial estimates with an updated empirical remote sensing product and perform extensive ground-truthing of seafloor with 2,542 diver surveys across remote sensing tiles. We also leverage seafloor assessments and movement data obtained from instrument-equipped tiger sharks, which have strong fidelity to seagrass ecosystems, to augment and further validate predictions. We report a consensus area of at least 66,000 km2 and up to 92,000 km2 of seagrass habitat across The Bahamas Banks. Sediment core analysis of stored organic carbon further confirmed the global relevance of the blue carbon stock in this ecosystem. Data from tiger sharks proved important in supporting mapping and ground-truthing remote sensing estimates. This work provides evidence of major knowledge gaps in the ocean ecosystem, the benefits in partnering with marine animals to address these gaps, and underscores support for rapid protection of oceanic carbon sinks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".