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Record W2791211080 · doi:10.1016/j.envpol.2018.03.037

Diatom responses to sewage inputs and hydrological alteration in Mediterranean streams

2018· article· en· W2791211080 on OpenAlexfundno aff
Elisabet Tornés, Jordi‐René Mor, Ladislav Mandarić, Sergi Sabater

Bibliographic record

VenueEnvironmental Pollution · 2018
Typearticle
Languageen
FieldMaterials Science
TopicDiatoms and Algae Research
Canadian institutionsnot available
FundersEuropean Regional Development FundFederación Española de Enfermedades RarasCanadian Institute for Advanced Research
KeywordsDiatomSTREAMSSewageBenthic zonePollutionMediterranean climateEnvironmental scienceEcologyEffluentBiologyEnvironmental engineering

Abstract

fetched live from OpenAlex

We analyzed the conjoint effects of sewage inputs and hydrological alteration on the occurrence of teratological forms and on the assemblage composition of stream benthic diatoms. The study was performed in 11 Mediterranean streams which received treated or untreated urban sewage (Impact sites, I), whose composition and morphological anomalies were compared to upstream unaffected (Control, C) sites. The impact sites had high concentrations of ammonium, phosphorus, and pharmaceutical compounds (antibiotics, analgesics, and anti-inflammatories), particularly in those receiving untreated sewage. Impact sites had a higher proportion of teratological forms as well as a prevalence of diatom taxa tolerant to pollution. The differences in the diatom assemblage composition between the paired C and I sites were the largest in the impacted sites that received untreated sewage inputs as well as in the systems with lower dilution capacity. In these sites, the diatom assemblage was composed by a few pollution-tolerant species. Mediterranean river systems facing hydrological stress are highly sensitive to chemical contamination, leading to the homogenization of their diatom assemblages.

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)0.001
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.017
GPT teacher head0.273
Teacher spread0.256 · 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

Citations44
Published2018
Admission routes1
Has abstractyes

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