Patterning Zooplankton Communities in Accordance with Annual Climatic Conditions in a Regulated River System (Nakdong River, South Korea)
Bibliographic record
Abstract
Abstract In this study, we investigated what environmental factors mainly influence the pattern of zooplankton communities in the regulated Nakdong River. Fifteen years of data (N= 629, 1994 to 2008) were used to analyze the patterns by means of self‐organizing map (SOM), that is capable of extracting information through competitive and adaptive properties. A total of 34 variables (18 groups of zooplankton and 16 physicochemical parameters) were patterned onto the SOM, and then each variable was compared in accordance with three different annual rainfall patterns (i.e., dry, moderate, and wet). Finally, seven clusters were categorized according to Euclidian distance between the clusters on the SOM. Clear seasonal features for the variables were interpreted in five major categories, and two more subcategories were partitioned by a specific feature of zooplankton abundance and appearance. In the result, the patterns of zooplankton assemblage were mainly distinguished by water temperature, Secchi transparency, and river flow rate. In particular, the SOM result depicted a clear pattern of zooplankton assemblage clusters partitioned by annual precipitation, which accentuated the prolonged effect of dam regulation on plankton assemblage in a regulated river system. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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 | 0.001 | 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 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".