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Record W3165972572 · doi:10.1002/agg2.20174

Assessing legacy effects of a 12‐year irrigated cropping systems study with a post‐hoc bioassay

2021· article· en· W3165972572 on OpenAlex
Francis J. Larney, Drusilla C. Pearson, Robert E. Blackshaw, Newton Z. Lupwayi

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAgrosystems Geosciences & Environment · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBioassayPhaseolusAgronomyCrop rotationCropMonocultureLegumeBiologyDry beanAnthesisTillageCultivar

Abstract

fetched live from OpenAlex

Abstract A healthy soil resource is vital to the continued success of irrigated agriculture in southern Alberta. A 12‐yr (2000–2011) irrigated cropping systems study was followed with a dry bean ( Phaseolus vulgaris L.) field bioassay in 2012, to assess legacy effects of preceding management. Specifically, a comparison of conventional (CONV) and conservation (CONS) management (reduced tillage, cover crops, compost addition, narrow‐row dry bean) legacies was sought. However, rotational legacies such as preceding phase, length, preceding crop, and interval since previous dry bean were also assessed by the fully phased experimental setup. Only 1 of 18 possible soil management contrasts (CONV vs. CONS, 2000–2011) was significant for the dry bean bioassay in 2012, despite overwhelming evidence of improved soil health (microbial biomass C and β‐glucosidase activity) under CONS management. Monoculture wheat ( Triticum aestivum L.) from 2000 to 2011 led to 2 d earlier maturity and higher disease incidence in bioassay dry bean. With wheat as a preceding crop, bioassay dry bean was significantly shorter (4 cm), earlier maturing (2 d), and lower yielding (by 21–35%) than with dry bean, potato ( Solanum tuberosum L.), or sugar beet ( Beta vulgaris L.) as preceding crops, largely due to volunteer wheat competition. Significantly enhanced bioassay yields (9–13%) with shorter intervals since previous dry bean demonstrated a “legume effect.” Overall, the dry bean bioassay was less effective at assessing soil management legacies (CONV vs. CONS) than rotational legacies such as preceding crop or interval since previous dry bean.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.017
GPT teacher head0.222
Teacher spread0.205 · 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