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Record W2756674455 · doi:10.1080/10934529.2017.1357408

Water quantity and hydrochemical quality monitoring of Laspias River, North Greece

2017· article· en· W2756674455 on OpenAlexaboutno aff
Georgios D. Gikas

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsTributaryEutrophicationWater qualityEnvironmental scienceAlkalinityChemical oxygen demandEffluentBiochemical oxygen demandPhosphorusEnvironmental chemistryHydrology (agriculture)WastewaterPollutionNutrientTotal dissolved solidsAmmoniumPoint source pollutionTotal suspended solidsNitrateSuspended solidsEnvironmental engineeringChemistryNonpoint source pollutionEcology

Abstract

fetched live from OpenAlex

The water quantity and quality of Laspias River, located in Xanthi Prefecture, Thrace, North Greece, are studied. A data collection program was designed at five stations along the main river course and its main tributary. Sampling frequency was about once every 10 days. Temperature (T), electrical conductivity (EC), pH, and dissolved oxygen were measured in situ simultaneously with discharge measurements. Moreover, water samples were collected and analyzed for the determination of nitrites, nitrates, ammonium, total phosphorus, biochemical oxygen demand, chemical oxygen demand, total suspended solids and alkalinity, according to standard methods. Anions (i.e., SO4−2 and Cl−) and cations (i.e., Li+, Na+, Mg+2, and Ca+2) were determined using ion chromatography (Dionex ICS-3000 ion chromatograph). The results showed that there is an impact of point (e.g., wastewater treatment plant effluent and farms) and non-point (e.g., agricultural activities) sources of pollution on the EC values, and chloride, nitrogen and phosphorus concentrations in the river water. The trophic state of Laspias River, based on nutrient concentrations, was found eutrophic. Based on the Canadian Council of Ministers of Environment Water Quality Index, the river water quality ranged from “poor” to “marginal” category, indicating the need for management measures in order to improve it.

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 score0.023
Threshold uncertainty score0.722

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 communication0.0000.001
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 head0.095
GPT teacher head0.375
Teacher spread0.281 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

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