MétaCan
Menu
Back to cohort
Record W329116498

Spring Snowmelt Impact on Phosphorus Addition to Surface Runoff in the Northern Great Plains

2011· article· en· W329116498 on OpenAlexaboutno aff
T. Jensen, Kevin H. D. Tiessen, Esther Salvano, Andrea R. Kalischuk, Don Flaten

Bibliographic record

VenueBetter crops with plant food · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSurface runoffEnvironmental scienceNutrientSnowmeltSurface waterAquatic ecosystemPhosphorusWater qualityHydrology (agriculture)AgronomyEcologyEnvironmental engineeringBiologyChemistry
DOInot available

Abstract

fetched live from OpenAlex

ovement of nutrients in surface runoff is a natural process in the environment. Under so-called pre- settlement conditions in the NGP, surface runoff naturally moved nutrients from grasslands, parklands, and forests. Nutrients in runoff exist primarily in either dissolved form or particulate form (attached to soil particles). Move- ment of nutrients in runoff is essential to aquatic ecosystem health as a source of nutrients for microbes, aquatic plants, and aquatic animals. The movement of nutrients from the landscape to water bodies, however, can be enhanced by human activities includ- ing agriculture, forestry, urbanization, industry, and recreation. These activities can promote nutrient loss through land clear- ing, and the application to land of fertilizers, manures, treated sewage, industrial waste effluents, and sludges. As an example, an 8-year water quality monitoring study of 23 agricultural watersheds in Alberta showed that as agricultural intensity

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 score0.320
Threshold uncertainty score0.636

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.189
Teacher spread0.175 · 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

Citations13
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueBetter crops with plant foodSame topicSoil and Water Nutrient DynamicsFrench-language works237,207