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Record W4363624515 · doi:10.5539/jps.v12n1p37

Allometric Equations for Estimation of Below-ground Biomass of Two Dominants Shrub Species of Burkina Faso

2023· article· en· W4363624515 on OpenAlexvenueno aff
Abdoulaye Tyano, Mipro Hien, Barthélémy Yélémou

Bibliographic record

VenueJournal of Plant Studies · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiomass (ecology)ShrubTree allometryAllometryDeforestation (computer science)Environmental scienceAgroforestryClimate changeBiologyForestryEcologyBotanyGeography

Abstract

fetched live from OpenAlex

Deforestation leads to a significant loss of carbon and contributes indirectly to climate change. This study was carried out in four types of formations in the Sudanian zone of Burkina Faso to assess the contribution of plant species to climate change mitigation. The below-ground biomass of two species (Piliostigma reticulatum and Guiera senegalensis) was determined by the direct method. Three classes of subjects were determined and a total of 80 shrubs of P. reticulatum and 90 shrubs of G. senegalensis were completely excavated. The results showed that P. reticulatum measures about 0.49 to 2.10 m in height, 3.58 to 25 cm in circumference at the base of the trunk and stores 0.18 to 3.68 tC/ha in the root biomass (respectively after 3 years and 15 years) for a 3x3m plantation. In the 15-year fallow dominated by G. senegalensis stands, 3.93 tC/ha are stored by the underground biomass of G. senegalensis shrubs. Model fit showed that there is a good correlation between circumference at the base of the trunk and below-ground biomass for P. reticulatum. For G. senegalensis, it is the total height of the foot that is most correlated with the below-ground biomass. These results provide information on the carbon sequestration potential of these two species, and can thus help in the decision-making process for climate change adaptation and/or mitigation policies.

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 score0.743
Threshold uncertainty score0.149

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.091
GPT teacher head0.308
Teacher spread0.217 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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