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Record W2030790739 · doi:10.1139/x06-227

Projected long-term productivity in Saskatchewan hybrid poplar plantations: weed competition and fertilizer effects

2007· article· en· W2030790739 on OpenAlexaffvenueabout
Clive Welham, Ken Van Rees, Brad Seely, Hamish Kimmins

Bibliographic record

VenueCanadian Journal of Forest Research · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsUniversity of SaskatchewanUniversity of British Columbia
Fundersnot available
KeywordsFertilizerWeedCompetition (biology)ProductivityAgronomyBiomass (ecology)Environmental scienceHuman fertilizationAgroforestryBiologyEcology

Abstract

fetched live from OpenAlex

An ecosystem management model was used to project total aboveground and belowground production in hybrid poplar plantations for two sites in Saskatchewan that were previously in agricultural production and that differed in their soil organic matter and nitrogen content (categorized as poor and rich sites). Stemwood production (the primary measure of treatment response) was always negatively affected by the competition that resulted when weeds were abundant; the effect was more severe on the poor than on the rich site. Stemwood biomass was greater when weed competition was low, but peak production declined over successive rotations on both sites, regardless of whether fertilizer was used. Fertilization always enhanced stemwood production but less so on the rich than on the poor site. A single fertilizer application in the second or seventh year after plantation establishment resulted in consistently higher stemwood production than midrotation fertilization (year 12). Fertilization was more beneficial to stemwood production when weed competition was high than when it was low. Low weed competition in conjunction with early fertilization produced the highest stemwood production. The simulations indicate that the relative benefit of a given management regime cannot be considered independently of the site nutrient status and the particular rotation.

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 score0.814
Threshold uncertainty score0.985

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 integrity0.0000.000
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 head0.033
GPT teacher head0.284
Teacher spread0.251 · 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

Citations46
Published2007
Admission routes3
Has abstractyes

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