Abundance and Diversity of Zooplankton along the Gulf of Mannar Region, Southeast Coast of India
Bibliographic record
Abstract
The study addresses the abundance and diversity of zooplankton along the Gulf of Mannar. Sampling was done from the nine stations. The samples were collected by horizontal hauls using the zooplankton net (150μm mesh size, 0.5 m mouth diameter and length 1.5 m) fitted with digital flow meter. From the study we recorded a total of 114 species of which the copepods formed the dominant group in all the stations. Which consists of Acartia spinicauda, Acartia danae, Pseudodiaptomus aurivilli, Eucalanus elongatus, Labidocera acuta, Nannocalanus minor, Paracalanus parvus, Corycaeus speciosus, Dioithona rigida, Oithona similis, Metis jousseaumei, Favella brevis and Tintinnopsis directa . The other dominant groups were barnacle nauplii, bivalves, gastropods larvae etc. The highest abundance (11,733 Nos./m3) was recorded in station 2, while the lowest (1,913 Nos./m3) was recorded in station 6. Diversity (H) was higher in the station 4 whereas the lowest diversity was observed from station 5 and no significant differences in Evenness (J) and Richness (SR) were observed between stations. A chemometric analysis such as cluster analysis (CA) and principal component analysis (PCA) reveals that, relationship among the zooplankton groups and studied stations. There was not any significant difference in zooplankton abundance between stations (p> 0.05).
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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) | 0.001 | 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".