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Record W2318890617 · doi:10.2166/wcc.2011.030

Long-term dynamics of water-borne nitrogen, phosphorus and suspended solids in the lower Don River basin (Russian Federation)

2011· article· en· W2318890617 on OpenAlexaff
Alexander V. Zhulidov, Juha Kämäri, Richard D. Robarts, D. D. Pavlov, Seppo Rekolainen, Tatiana Gurtovaya, Jarmo J. Meriläinen, V. V. Lugovoy

Bibliographic record

VenueJournal of Water and Climate Change · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAquatic and Environmental Studies
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsPhosphorusPhytoplanktonEnvironmental scienceNutrientNitrogenSuspended solidsDrainage basinHydrology (agriculture)Algal bloomEutrophicationOceanographyEcologyEnvironmental engineeringChemistryGeologyBiologyGeographyWastewater

Abstract

fetched live from OpenAlex

A long-term study (1986–2002) of water-borne nutrient and suspended solids dynamics was undertaken on the lower Don River, which plays an extremely important role in the water supply of the Black Sea and Azov Sea basin. Suspended solids were greatest in spring and summer and were correlated to river discharge. Mean annual nitrogen concentrations increased from 1986 to 1995 and then decreased from 1996 to 2002. Unlike nitrogen, phosphorus concentrations (both phosphates and total phosphorus) gradually increased throughout the study period changing the river from an oligotrophic to upper mesotrophic status. If this trend continues phytoplankton could become nitrogen-limited leading to the development of nitrogen-fixing cyanobacterial blooms. No obvious relation between fertiliser usage over the Rostovskaya Oblast and nutrient dynamic patterns was identified, probably because only 10% of the water in the river comes from this area. The reason for the unusual and contradictory nitrogen and phosphorus changes remain largely unknown for this regulated river.

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 score0.054
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.030
GPT teacher head0.211
Teacher spread0.182 · 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

Citations6
Published2011
Admission routes1
Has abstractyes

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