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Record W4404109276 · doi:10.57890/ydd70f52

Urban waste-water treatment plants as hotspots for birds : an environmental assessment highlights the role of a single dominant gull

2024· article· en· W4404109276 on OpenAlexfundno aff
Fulvio Cerfolli, Corrado Battisti

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
FundersUniversity of OxfordUniversity of CambridgeMcGill University
KeywordsEnvironmental planningGeographyEnvironmental scienceEnvironmental resource managementEnvironmental protection

Abstract

fetched live from OpenAlex

Waste-water treatment plants (WWTPs) are still little explored in their ecological role. This work reports data obtained from standardized sampling for the two largest Italian WWTPs, to obtain seasonal patterns (late winter, spring, and autumn) of univariate diversity metrics in bird communities. The ecological conditions, linked above all to the high availability of water, with the presence of mud and trophic resources, and heterogeneous features (buildings, trees, and hedges), allow the presence of water-related and synanthropic bird species, using the site as a seasonal stopover and wintering sites. The large availability of biomass of invertebrates, linked to the sludge from the water-waste treatment plants, can provide an important trophic resource during spring migration. The richness, diversity and evenness did not differ significantly between the two plants analysed, both having comparable size and heterogeneity. The highest Simpson dominance values were recorded in autumn with species frequency concentrated in a few abundant species. Detrended Correspondence Analysis (DCA) shows a close association between the autumn period and the dominant Black-headed Gull (Chroicocephalus ridibundus), in both treatment plants, with possible implication on the spreading on zoonosis.

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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.266
Teacher spread0.249 · 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
Published2024
Admission routes1
Has abstractyes

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