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Record W2498526790 · doi:10.5539/jas.v8n8p201

Biomass Distribution and Development of Allometric Equations for Non-Destructive Estimation of Carbon Sequestration in Grafted Mango Trees

2016· article· en· W2498526790 on OpenAlexvenueno aff
A. N. Ganeshamurthy, V. Ravindra, R. Venugopalan, Malarvizhi Mathiazhagan, R. M. Bhat

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsAllometryTree allometryBiomass (ecology)Diameter at breast heightMathematicsSampling (signal processing)BotanyAnimal scienceBiologyAgronomyEcologyPhysicsBiomass partitioning

Abstract

fetched live from OpenAlex

<p>The general equations available/developed for forest/wild mango trees based on measurement of diameter at breast height (DBH) (cannot be used) are not applicable for mango orchards which are predominantly established with grafted plants. Hence allometric equations were developed with destructive sampling of grafted mango trees. The selected parameters showed that allometric parameters were significantly related with age of the trees. The proportion of roots (22%) in grafted mangos was found to be higher than those reported for tropical forest trees (18%) with a R ratio of 0.291. The biomass expansion factor (BEF) varied with age. Initially the BEF was very high followed by a decreasing phase and finally a steady phase by and large attained stability beyond 20 years. The equations generally fitted the data well, and in most cases more than 50% of the observed variation in biomass was explained by primary branch girth (PBG) × number of primary branches (NPB). All equations were statistically significant (p < 0.05) for both scaling parameters, a and b. Based on the R<sup>2</sup> values the best fit model for estimation of above ground biomass of grafted mango trees is a power model using PBG × NPB as the best dimension: There was a good agreement between the observed and the predicted biomass using this equation.</p>

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)0.000
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 score0.884
Threshold uncertainty score0.105

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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

Citations25
Published2016
Admission routes1
Has abstractyes

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