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Record W4396867404 · doi:10.53555/sfs.v10i1.2706

An Economic Efficiency Of Ground Water In Pudukottai District, Tamil Nadu

2023· article· en· W4396867404 on OpenAlexvenueno aff
R. Suresh, N. Saravanakumar

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsTamilGroundwaterWater resource managementGeographyEnvironmental scienceAgricultural economicsEconomicsGeologyArtGeotechnical engineering

Abstract

fetched live from OpenAlex

Ground water has made significant contributions to the growth of India’s Economy and has been an important catalyst for its socio economic development. The State has as an area of 1.3 Lakh sq.km with a gross cropped area of around 63 L. Ha.. The Government's policy and objectives have been to ensure stability in agricultural production and to increase the agricultural production in a sustainable manner to meet the food requirement of growing population and also to meet the raw material needs of agro based industries, there by providing employment opportunities to the rural population. Tamil Nadu has all along been one of the states with a creditable performance in agricultural production with the farmersrelativelymoreresponsiveandreceptivetochangingtechnologies and market forces. Water used for irrigation should be essentially in good quality to grow good quantity crops, for the maintenance of soil productivity and for the protection of the environment. Physical and mechanical properties of soil, soil structure and permeability are very sensitive to the type of exchangeable ions present in irrigation water. Today, groundwater irrigation is becoming the cornerstone of providing water for agriculture, resulting in an overall exploitation rate of over 85%of the total available resources. Declining rates of tank and canal irrigation and overexploitation of groundwater are so critical that the state needs new policy interventions to tackle a pending water crisis. This policy brief recommends some development and investment options for the irrigated sector in Tamil Nadu.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.103
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.072
GPT teacher head0.231
Teacher spread0.159 · 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 source (direct Gemma or distilled Codex), 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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