Characteristics of Large-Scale Harmful Algal Blooms (HABs) in the Yangtze River Estuary and the Adjacent East China Sea (ECS) from 2000 to 2010
Bibliographic record
Abstract
Harmful algal blooms (HABs) are a serious worldwide issue which has posed great risks on marine ecosystems and public health by directly releasing toxins or indirectly leading to anoxia in marine environment. In recent years HABs have caused huge economic losses in China, particularly in the Yangtze Estuary and the adjacent East China Sea (ECS). The present study investigated the spatial-temporal and species characteristics of large-scale HABs in this area using geographic information system (GIS) Kernel Density Estimation (KDE) spatial analysis, statistical methods and satellite image interpretation. Results revealed that the Yangtze Estuary, Zhoushan island, Xiangshan bay and Jiushan island are the regions with highest frequency of large-scale HABs. HABs in the ECS reached a peak in terms of total number and area in 2003 to 2005 and occupied a high percentage (around 70% in area and 60% in occurrence) in the four Chinese coastal waters. The number of large-scale HABs (> 1000 km2) in the Yangtze Estuary and the adjacent ECS declined after 2005 while that of HABs (> 100 km2) declined after 2008. Large-scale HABs occurrences concentrated in summer (May to July), and the averaged duration increased continually from the shortest time (1.3 days) in 2001 to the longest (10.9 days) in 2010 for each HAB. 17 causative species were found with Prorocentrum dentutam as the most frequent dominant species, followed by Skeletonema costatum, Karenia mikimotoi, and Chaetoceros curvisetus. Water discoloration observed in MODIS satellite true color images was well consistent with the corresponding HABs reported by State Oceanic Administration of China (SOA). Multiple factors involving eutrophication, physical dynamics, topography and deposition conditions contributed to the formation of frequent HABs in the ECS. Three strategies including establishing a synthesized system, improving the previous database and investigating multiple contributors were proposed for future HABs monitoring and management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".