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Record W2113390535 · doi:10.1139/x06-205

Nutrient cycling in a poplar plantation (<i>Populus trichocarpa × Populus deltoides</i>'Beaupré') on former agricultural land in northern Belgium

2007· article· en· W2113390535 on OpenAlexvenueno aff
Linda Meiresonne, An De Schrijver, Bruno De Vos

Bibliographic record

VenueCanadian Journal of Forest Research · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental sciencePlant litterNutrientNutrient cycleCyclingAgronomyGrowing seasonLeaching (pedology)Deposition (geology)SalicaceaeNitrogenNitrogen cycleBiomass (ecology)LoamSoil waterWoody plantBotanyChemistryEcologySoil scienceForestryBiology

Abstract

fetched live from OpenAlex

Hydrological fluxes, atmospheric deposition, litterfall, and soil percolation of the most important nutrients were measured in an 18-year-old poplar plantation on a well-drained silt loam soil during 2 consecutive years. Downward soil water flux and transpiration are the most important factors in the water balance. Around 80% of total nitrogen input (6.6 and 6.5 kmol·ha –1 in years 1 and 2, respectively) originates from litterfall. After nitrification only a negligible amount of nitrate leaches during the growing season. Yearly uptake of nitrogen by the poplar ecosystem (woody biomass, leaves, and ground vegetation) approximately equals the input, of which more than 50% is accounted for by the leaves. This indicates very efficient nitrogen cycling. Total deposition of base cations originates from two processes, dry deposition (Mg 2+ and Ca 2+ ) and canopy leaching (K + and Ca 2+ ). Litter input of Ca 2+ represents about 83% of the total input (stand deposition + litterfall), Mg 2+ about 61%, and K + less then 50%. Percolation of base cations at 1 m depth is very limited. Rather high Ca 2+ and K + contents of the woody biomass can lead to high exports at harvest. Nutrient cycling in the poplar stand proved to be very efficient, with no significant nutrient losses.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.037
GPT teacher head0.279
Teacher spread0.243 · 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

Citations45
Published2007
Admission routes1
Has abstractyes

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