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Record W4292882174 · doi:10.1002/ael2.20088

Assessing how cover crops close the soil health gap in on‐farm experiments

2022· article· en· W4292882174 on OpenAlex
Fernanda Souza Krupek, Steven Mugisha Mizero, Daren D. Redfearn, Andrea Basche

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAgricultural & Environmental Letters · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Guelph
FundersNatural Resources Conservation Service
KeywordsEnvironmental scienceSoil waterSoil healthCover cropSoil coverAgronomyInfiltration (HVAC)Soil scienceSoil organic matterHydrology (agriculture)AgroforestryGeographyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Assessing the success of cover crops (CCs) as a way to promote soil health at the farm scale remains a challenge. At four on‐farm CC experiments in Nebraska, we quantified soil health relative to a reference soil. We examined physical, chemical, and biological properties in near‐surface soil. Cover crops reduced the soil health gap between bare (no‐CC) and reference soil in the short (3‐yr) timescale, but the magnitude of responses depended on cropland management history and ecological dynamics of reference site plant communities. Increases in soil health relative to reference soils showed some relationship to increases in soybean [ Glycine max (L.) Merr.] and corn ( Zea mays L.) yields. Clear discrimination of reference from bare soils was most influenced by organic matter and infiltration measurements conducted under the highest sampling intensity. Framing soil metrics relative to reference soils and ensuring appropriate sampling intensity are important to quantify the effects of CC on farm landscapes.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.529

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 studies0.0010.000
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 head0.020
GPT teacher head0.227
Teacher spread0.207 · 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