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Record W4380894961 · doi:10.53555/sfs.v10i1.550

Physico-Chemical Analysis Of Fish Farming Ponds Of Darbhanga, Bihar, India

2023· article· en· W4380894961 on OpenAlexvenueno aff
Arti Kumari, Namrata Prasad, Kumari Shachi, Sanjeev Kumar

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFisheries and Aquaculture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTurbidityWater qualityPotassiumEnvironmental scienceProductivityAgricultureFish farmingFish pondSodiumAlkalinityNutrientFish <Actinopterygii>ZincChlorideFisheryAquacultureBiologyChemistryEcology

Abstract

fetched live from OpenAlex

&nbsp; Safe drinking water is a fundamental human right &amp; basic need of individuals. Water quality is an important criteria for productivity of pond. The study was designed to assess the quality of pond water of Dighi Pond &amp; Harahi pond of Darbhanga, Bihar, with reference to physic-chemical parameters including turbidity, conductivity, dissolve O2, free CO2, PH, potassium, zinc, iron, sodium, chloride and hardness. The results were evaluated and compared with both ponds water quality. It is found that based on physic-chemical parameters, both ponds water is not suitable for fish farming. &nbsp;

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)0.001
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 score0.089
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.006
Science and technology studies0.0000.001
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 head0.137
GPT teacher head0.271
Teacher spread0.134 · 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
Published2023
Admission routes1
Has abstractyes

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