Simulation on the sediments affecting <i>Microcystis</i> recruitment in north bay of Lake Dianchi
Bibliographic record
Abstract
滇池北部福保湾主要承接上游昆明市的生活污水及周边工业污水,其污染程度极为严重.本研究在福保湖湾内设置4个采样点,分别采集了不同区域的沉积物,首次模拟研究了微囊藻(Microcystis)在不同沉积物环境中复苏能力差异,结果表明微囊藻在模拟实验中的复苏能力表现出对不同底质的不同适应性,入口湖区的沉积物对微囊藻的复苏有极强的抑制作用.藻类复苏后达到的最大生物量(以叶绿素a计)分别为东岸对照区的4.7%,西岸对照区的6.6%及吹填区的11.9%,其中微囊藻生物量也远低于其它各样点,占东岸对照、吹填区及西岸对照的比例分别为5.2%、10.3%和19.4%.以上研究暗示了河口处沉积物不适合微囊藻的复苏.福保湾藻类水华的种源贡献应该主要依靠外源性输入,即湖流场和风向所导致的藻类水平迁移贡献远远大于底泥复苏至水体的垂直迁移.;Fubao Bay is located in the north of Lake Dianchi, which is one of the most seriously polluted bays in the lake. Two mainrivers, Haihe River and Daqinghe River flow into Fubao Bay from the north. Four sampling sites were settled in this bay, and thesediment samples were collected by using Petersen grab. For the first time, the study of recruitment ability of Microcystis in differentsediment environments was carried out in simulation devices. The results in the present study showed that the recruitment ability ofMicrocystis was quite different depended on its habitat environments. The Microcystis recovery was inhibited in lacustrine sedimentsnear estuary, and the maximum biomass (calculated as chlorophyll-a) was pretty lower than other sampling sites. Compared amongEast Coast Area, West Coast Area and Hydraulic Mud Fill Area, the Microcystis biomass were only 4.7%, 6.6% and 11.9%,respectively. And Microcystis biomass (calculated as Microcystis cell numbers) were about 5.2%, 10.3% and 19.4%, corresponding.All the results indicated that sediments in estuary of Fubao Bay were not appropriate for Microcystis recruitment. The contribution of“seeds bank” recruiting to form HABs in this bay could be much less than the wind-induced external loading. All these results couldbe helpful in Microcystis blooms forecasting, preventing and controlling in the future.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".