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Record W2012441801 · doi:10.5380/rf.v44i3.28142

APORTE DE BIOMASSA E NUTRIENTES POR Allagoptera arenaria NA RESTINGA DA MARAMBAIA, RIO DE JANEIRO, RJ

2014· article· pt· W2012441801 on OpenAlex

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.

Bibliographic record

VenueFLORESTA · 2014
Typearticle
Languagept
FieldEnvironmental Science
TopicEnvironmental and biological studies
Canadian institutionsMedicine Hat College
Fundersnot available
KeywordsNutrientShrubDry matterOrganic matterHorticultureBiologyAnimal scienceBotanyEcology

Abstract

fetched live from OpenAlex

O estudo avaliou a quantidade de matéria orgânica e nutrientes foliares aportados ao solo por Allagoptera arenaria em uma comunidade arbustiva de Palmae na restinga de Marambaia, Rio de Janeiro. As coletas foram realizadas em dez parcelas de 10 m x 10 m (100 m²), demarcadas aleatoriamente na formação arbustiva. Foram coletadas 3 (três) folhas de dez indivíduos adultos escolhidos aleatoriamente. Foi possível quantificar um incremento anual de massa seca de 8,2 Mg.ha-1 ano-1 para a área de estudo. Para o N, observou-se um teor de 12,3 g.kg-1, seguido de 5,7 g.kg-1 para o K, os quais representam, respectivamente, um aporte de 303 kg.N.ha-1.ano-1 e 140 kg.K.ha-1.ano-1. Para P, foi verificado teor no material foliar na ordem de 0,31 g.kg-1, com aporte de 7,4 kg.ha-1.ano-1.Palavra-chave: Ciclagem de nutrientes; ecossistemas costeiros; palmáceas; plantas focais. AbstractBiomass and Nutrient input by Allagoptera arenariain in Restinga da Marambaia, Rio de Janeiro, RJ. This research assessed the amount of organic matter and foliar nutrients in the soil from Allagoptera arenaria in a Palmae shrub community of Restinga da Marambaia, Rio de Janeiro. Samples were collected at ten randomly demarcated plots of 10 x 10m (100 m²) in the bush. We collected three (3) leaves of ten randomly chosen adults. It was possible to quantify an annual increase of dry mass of 8,2 Mg ha-1 yr-1 for the focused area. We observed for N an average grade of 12,3 g kg-1 followed by 5,7 g kg-1 for K, which respectively represent an investment of 303 kg N ha-1 year-1 and 140 kg K ha-1 year-1. We observed for P mean levels in leaf material in order of 0,31 g kg-1 with intake of 7,4kg ha-1 year-1.Keywords: Nutrient cycling; coastal ecosystems; palms; nurse plants.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.018
GPT teacher head0.225
Teacher spread0.208 · 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