Bibliographic record
Abstract
采用多因子實驗設計 ,經三維ANOVA統計分析 (TukeyTest) ,研究了溫度、鹽度和光照強度對魚毒類赤潮藻———赤潮異彎藻Heterosigmaakashiwo生長的影響。結果表明 ,在實驗范圍溫度 (1 2℃、1 9℃、2 5℃、3 2℃ ) ,鹽度 (1 0、1 8、2 5、3 0、3 5 )和光照強度 0 0 2× 1 0 1 6、0 0 8× 1 0 1 6、0 3× 1 0 1 6、1 6× 1 0 1 6quanta/(cm2 ·s)內 ,各溫度和光照強度、以及低鹽度 (1 0 ,1 8)和高鹽度 (3 5 )之間 ,藻生長率有極顯著差異 (P <0 0 0 1 )。光照強度和溫度、鹽度和溫度以及這三個因子之間存在極顯著的相互作用 (P <0 0 0 1 ) ,而光照強度和鹽度之間無顯著的相互作用 (P =0 0 74>0 0 5 ) ,藻生長的最適溫度、光照、鹽度條件分別為 2 5℃、1 6× 1 0 1 6quanta/(cm2 ·s)、1 0— 3 5 ,這時生長率為 0 .85d- 1 。Raphidophyte Heterosigma akashiwo is a fish-killing red tide species, which widely distributes in the world and has caused great economic loss in many countries such as Japan, Canada and New Zealand.In Dalian Bay, China, it caused red tide each summer during 1985-1987 and has been also found in Jiaozhou Bay and Dapeng Bay in recent years.In order to understand the mechanism of red tide formation, it is important to know more about its growth characteristics. This paper used 3-factor experiment design to study the effects of temperature, salinity and irradiance on the growth of this important species H.akashiwo (isolated from Jiaozhou Bay, Qingdao, China) . The results show that the growth of H.akashiwo was significantly different among temperatures (12, 19, 25, 32℃) , irradiance[0.02×10 16, 0.08×10 16, 0.3×10 16, 1.6×10 16quanta/ (cm 2·s) ]and between low salinity (10, 18) and high salinity (35) .There were interactive effects between any two of and among all three physical factors on the growth of H.akashiwo, except irradiance and salinity.The optimal growth condition of H.akashiwo was 25℃, 10-35and 1.6×10 16quanta/ (cm 2·s) with a growth rate of 0.85d -1during the exponential growth phase.Therefore, H.akashiwo could divide at high rate and is more likely to bloom under high temperature and high illumination in summer, and this species is able to distribute widely in the ocean and estuaries due to its adaptation to wide salinities.The paper also discusses red tide formation mechanism and possible wide distribution of this species in China.To protect mariculture industry and fishery resources, more attention should be paid to this fish killing species.
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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 | 0.000 | 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 | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".