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Record W2508688649 · doi:10.1515/boku-2016-0008

Simulated future changes of extreme nutrient loads in a mesoscale agricultural watershed in Bavaria / Simulierte zukünftige Änderungen der Extremwerte für Nährstofffrachten in einem mesoskaligen landwirtschaftlichen Einzugsgebiet in Bayern

2016· article· en· W2508688649 on OpenAlexafffund
Bano Mehdi, Ralf Ludwig, Bernhard Lehner

Bibliographic record

VenueDie Bodenkultur Journal of Land Management Food and Environment · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWatershedEnvironmental sciencePercentileStreamflowPrecipitationHydrology (agriculture)SWAT modelNutrientGeographyDrainage basinMeteorologyEcologyBiologyMathematicsStatistics

Abstract

fetched live from OpenAlex

Summary The hydrological model SWAT was applied to the upper Altmühl watershed to examine the simulated 10th and 90th percentiles of streamflow, nitrate nitrogen (NO3 --N), and total phosphorus loads (TP), using an ensemble of reference climate (1975–2000) and future climate (2046–2070) simulations. A comparison between the two periods showed that in the future, the 90th percentiles of the NO3 --N loads increase in all seasons which indicates a trend to increasing diffuse pollution in the mid-term future. Conversely, the 90th percentile TP loads diminished in winter and otherwise remained similar to the reference period. The 10th and 90th percentile changes in the future streamflow followed changes in precipitation, and did not have any apparent influence on extreme nutrient transport events.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.228

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.009
GPT teacher head0.200
Teacher spread0.190 · 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 designSimulation or modeling
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 routes2
Has abstractyes

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