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Record W2968321894 · doi:10.31807/tjwsm.549386

Drinking Water Treatment Plants in Turkey and Determination of Revision Needs

2019· article· en· W2968321894 on OpenAlexaff
Esra Giresunlu, Arife Özüdoğru, Fulya Yaycılı, Hilal Uflaz, Cenk GÜMÜŞKAYA, Murat Sarıoğlu, Yakup Karaaslan

Bibliographic record

VenueTurkish Journal of Water Science and Management · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsChristian ministryWater qualityWater Framework DirectiveWork (physics)Water consumptionWater treatmentDirectiveEnvironmental protectionEnvironmental scienceBusinessEnvironmental planningEnvironmental engineeringEngineeringPolitical scienceComputer scienceBiologyEcology

Abstract

fetched live from OpenAlex

Drinking water quality is regulated by Regulation Concerning Water Intended for Human Consumption by Ministry of Health in Turkey. As in the process of becoming a member state of EU, Turkish regulation is compatible with EU Drinking Water Directive. This study revealed that there are 489 drinking water treatment plants, 397 of which are under operation. Within this work, identity cards for these active plants were prepared. Process selection of these plants were specified. A GIS-based program called ISBIS is developed and all data collected within this work is uploaded to the database. Site-visits to 193 selected plants were conducted and site-visit reports focusing on operational and structural issues at these plants are issued. As a result of this work, it appeared that 37 drinking water treatment plants out of 193 site-visited need to be reconstructed. Also, it was observed that most of the drinking water treatment plants are not capable of removing micropollutants, and will need major revisions.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.182

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.015
GPT teacher head0.255
Teacher spread0.240 · 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 designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

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