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Record W2305806963 · doi:10.5376/ijms.2016.06.0003

Using Two Species From Common Aquatic Plants as Bio-indicators of Pollution With Hydrocarbons Compounds in Al-Kahlaa River -Missan Province/ Iraq

2016· article· en· W2305806963 on OpenAlexvenueno aff
Al-Saad Hamid.T.

Bibliographic record

VenueInternational Journal of Marine Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsDry weightPollutionCeratophyllum demersumPollutantAquatic plantEnvironmental scienceSeasonalitySpatial variabilityEnvironmental chemistryBiologyEcologyBotanyChemistry

Abstract

fetched live from OpenAlex

Two species from common aquatic plants ( Ceratophyllum demersum and Paspalum pespaioides ) were used as bio-indicators of pollution with hydrocarbons compounds at Al-Kahlaa River in Missan province .Plant samples were collected during the period from October 2012 to November 2013. Concentrations of Total Petroleum Hydrocarbons (TPHs) in C. demersum ranged between 5 µg/g dry weight during winter and 58.97 µg/g dry weight during summer, whereas in P. pespaioides ranged between 3.18 µg/g dry weight during winter and 43.44 µg/g dry weight during summer. Also results of this study revealed a seasonal and spatial variations in concentrations of TPHs in both plants, the highest concentrations were recorded during summer whereas the lowest during winter. Results of statistical analysis revealed that there is no significant spatial variation whereas there is a significant seasonal variation in C. demersum . While results in P. pespaioides revealed that there was a significant spatial variation, whereas there is a significant seasonal variation. These plants are capable of accumulating TPHs and can be used as bio-indicators for monitoring pollution by this type of pollutants in aquatic environment.

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)0.000
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 score0.235
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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