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Record W2786246916 · doi:10.6000/1927-5129.2017.13.104

Preliminary Studies on the Occurrence and Abundance of Zooplankton Major Taxa in Keamari, Karachi- Backwaters

2017· article· en· W2786246916 on OpenAlexvenueno aff
Qadeer Mohammad Ali, M. A. Azmi

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFish Biology and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsZooplanktonDiversity indexAbundance (ecology)BiologyMonsoonCopepodSpecies richnessSalinityEcologyShrimpOceanographyFisheryCrustacean

Abstract

fetched live from OpenAlex

A preliminary study was conducted on the occurrence and abundance of zooplankton in the Karachi backwaters. Zooplankton sampling was conducted on monthly basis and the study was carried out on the basis of three seasons including pre-monsoon (January to May), monsoon (June to September) and post-monsoon (October to December) from a permanent station Napier Mole bridge (24o50’34’’.90 N, 66o59’17’’.55 E) during June 1996 to May 1998. The hydrographic parameters including air temperature (oC), water temperature (oC), salinity (ppt), dissolved oxygen (mg/L), pH, and transparency (cm) were recorded. Total 14 groups of zooplankton were recorded; hydromedusae, copepoda, mysids, amphipoda, acetes, lucifer, chaetognath, penaeid pl, caridean pl, zoea, megalopa, squilla larvae, fish larvae, fish eggs and others. Pre-monsoon season shows highest number of individuals and copepods were found to be the dominant group in all seasons. Zooplankton diversity, equitability and margalef index were measured seasonally. Highest shannon – wiener diversity index H' = (1.83), equitability E= (0.69) and margalef species richness Index d= (1.37) were measured in post-monsoon season. Statistical analysis (ANOVA) was performed in between seasons and zooplanktonic groups. No significant difference (at P>0.05, 0.148) was observed between zooplankton and seasons.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.273
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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