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Record W2967294490 · doi:10.1007/s40093-019-00292-w

Quantifying the influence of eucalyptus bark and corncob biochars on the physico-chemical properties of a tropical oxisol under two soil tillage modes

2019· article· en· W2967294490 on OpenAlexafffund
Boris Merlain Djousse Kanouo, Suzanne Allaire, Alison D. Munson

Bibliographic record

VenueInternational Journal Of Recycling of Organic Waste in Agriculture · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversité LavalMinistère des Ressources naturelles et des Forêts
FundersUniversité Laval
KeywordsOxisolBiocharCation-exchange capacityBulk densityHydraulic conductivityPhosphorusEnvironmental scienceAgronomyStrawTillageSoil waterChemistrySoil sciencePyrolysis

Abstract

fetched live from OpenAlex

This study aimed to assess the impact of two biochars applied at the rate of 15 t ha −1 on physico-chemical parameters of an oxisol in Cameroon. The biochars were made from slow pyrolysis (~ 300 °C, 4 h) of eucalyptus tree bark and corncobs and then incorporated into the top 15 cm of the soil with or without straw. The soil tillage mode was either flat plots or furrows and ridges. Soil porosity, bulk density, saturated hydraulic conductivity, available water content, pH, nitrogen, potassium, phosphorus, cation exchange capacity and electrical conductivity were analysed before biochar application, then 6 and 12 months after. None of the measured soil physical parameters were affected by the presence or type of biochar. The total porosity was lower during the second production period compared to the first, while available water content and van Genuchten parameters increased during the second production period. No significant difference was observed between soil nitrogen, phosphorus, potassium, cation exchange capacity and electrical conductivity of control and treated plots. We recommend that straw be pyrolysed and the resulting biochar incorporated into soil instead of burying straw (as is actually done in furrow and ridges tillage mode).

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.136

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 studies0.0000.000
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 head0.022
GPT teacher head0.233
Teacher spread0.211 · 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 designBench or experimental
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

Citations15
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal Of Recycling of Organic Waste in AgricultureSame topicSoil Carbon and Nitrogen DynamicsFrench-language works237,207