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Record W2772561836 · doi:10.1515/ohs-2017-0037

Habitat requirements of <i>Elodea canadensis</i> Michx. in Polish rivers

2017· article· en· W2772561836 on OpenAlexaboutno aff
Aurelia Cegłowska, Szymon Jusik, Aleksandra Samecka-Cymerman, Agnieszka Klink, Krzysztof Szoszkiewicz

Bibliographic record

VenueOceanological and Hydrobiological Studies · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsElodea canadensisMacrophyteEutrophicationHabitatAquatic plantNutrientEnvironmental scienceEcologyHydrology (agriculture)BiologyGeology

Abstract

fetched live from OpenAlex

Abstract The main objectives of this work were to investigate the range of habitat conditions (in terms of water chemistry and hydromorphological parameters) at sites colonized by Elodea canadensis and to analyze the species composition of communities with this plant species. We analyzed physicochemical, hydromorphological and biological data from 1135 sites located in Polish rivers to identify environmental factors that determine the occurrence of Elodea canadensis . Canadian waterweed was present at 18.1% of the analyzed river sites, located mainly in the lowlands (26.2% of all lowland sites).The results show that Elodea canadensis prefers moderately mineralized water (545 ± 329 μS cm −1 ), rich in calcium and magnesium carbonates (174 ± 63 mg CaCO 3 l −1 , 84.1 ± 31.4 mg Ca 2+ l −1 and 11.1 ± 6.4 mg Mg 2+ l −1 ), with moderate concentrations of chlorides and sulfates (38.9 ± 59.1 mg Cl - l −1 and 62.3 ± 50.9 mg SO 4 2− l −1 ) and in terms of nutrients, it prefers water from moderately rich to mesotrophic and eutrophic. This plant has high light requirements and grows mainly in unshaded sections of shallow rivers. The studied species avoids sections of rivers strongly transformed and those with reinforced banks and bottoms. Canadian waterweed occurs mostly in the company of vascular macrophytes associated with slow-flowing rivers with sandy bottom material, indicating mesotrophic and eutrophic water.

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 score0.012
Threshold uncertainty score0.409

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 communication0.0000.000
Open science0.0000.001
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 head0.044
GPT teacher head0.282
Teacher spread0.238 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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