Accurate mapping of seaweed farms with high-resolution imagery in China
Bibliographic record
Abstract
Seaweed aquaculture is vital in protecting the marine eco-environment and mitigating climate change. China generates more than half of the world's total seaweed production. However, despite multiple local studies, accurate and reliable information on broad-scale seaweed farms is still scarce. Using an object-based method to classify 3 m spatial resolution Planet Scope images along offshore China, a total of 129 494 ha of cultured seaweed was identified and delineated with an overall accuracy of 95.70% and a KAPPA index of 0.912, respectively. Then, a seaweed map in offshore China in 2018-2019 was developed. The results provided basic information about seaweed farms in China. The approach reported in this work is accurate and efficient, which can be used to replace the conventional method to obtain the culture seaweed information. This study can be of reference for mapping seaweed on a broader or global scale.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 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".