Application of fuzzy cluster analysis to Lake Simcoe crustacean zooplankton community structure
Bibliographic record
Abstract
Fuzzy clustering generates cluster membership weights that indicate how tightly each object is linked to its cluster relative to other clusters of a dendrogram. In a fuzzy clustering of the crustacean-zooplankton taxa of Lake Simcoe, a large (720 km2) hardwater lake in Ontario, Canada, we show how the membership weights can be used to rank all taxa for their contribution to the sampling unit (SU) classification, where the total number of SUs was 84 (7 years × 12 sampling sites). The validity of the results was confirmed by comparison with other more traditional methods of identifying variables important for object classifications and by permutation tests of matrix correlation before and after removal of low-ranked and highly ranked species. Fuzzy clustering of Lake Simcoe SUs also revealed (i) the likelihood of trends in zooplankton community composition over the 7-year period and (ii) differences in composition possibly related to sampling-station depth. In particular, the shallowest sampling station in southern Cook's Bay had a zooplankton community structure that differed significantly from other stations during all years of the study. As a preliminary screening or data exploration tool, fuzzy clustering is particularly useful for analysis of ecological data.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".