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Record W2918566079 · doi:10.1016/j.dib.2019.103790

Soil 16S DNA sequence data and corresponding soil property and wheat yield data from a 72-plot field experiment involving pulses and wheat crops grown in rotations in the semiarid prairie

2019· article· en· W2918566079 on OpenAlexafffund
Chantal Hamel, Yantai Gan, D. Messer, Luke D. Bainard

Bibliographic record

VenueData in Brief · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsAgriculture and Agri-Food Canada
FundersGovernment of CanadaSaskatchewan Pulse Growers
KeywordsYield (engineering)Plot (graphics)AgronomyField (mathematics)Property (philosophy)Environmental scienceMathematicsBiologyPhysicsStatistics

Abstract

fetched live from OpenAlex

The soil bacteria diversity and corresponding environmental data made available here are from a 72-field plot experiment testing the effect of pulse frequency in nine wheat-based rotation systems, in the semiarid prairie. The data include sequences of the V6-V8 regions of bacterial 16S rDNA from soil and root extracts, generated using Roche GS FLX Titanium technology, and associated environmental data, specifically levels of soil organic carbon, total carbon, total nitrogen, total phosphorus, pH, electrical conductivity, and extractible sulfate sulfur, copper, iron, manganese, zinc, potassium, nitrate nitrogen, phosphate phosphorus, calcium, and magnesium in the 0-15 cm soil layer, and mineral nitrogen and phosphate in the 0-120 cm soil layer. The grain yield of wheat in the last (4th) phase of the crop rotation systems is also given. The data can be used in meta-analyses of the effect of pea, lentil and chickpea in wheat-based cropping systems on soil bacterial diversity or for monitoring the evolution of soil bacteria communities in cultivated prairie soils in the context of climate change. Samples were collected between 2012 and 2014.

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 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)0.001
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 score0.564
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.002
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 head0.119
GPT teacher head0.297
Teacher spread0.179 · 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 teacher head, 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

Citations4
Published2019
Admission routes2
Has abstractyes

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