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Record W7095797768

1 TREE GROWTH AND CROP PRODUCTIVITY IN A HYBRID POPLAR- HARDWOOD-SOYBEAN INTERCROPPING SYSTEM IN SOUTHWESTERN

2015· article· en· W7095797768 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgroforestry and silvopastoral systems
Canadian institutionsnot available
Fundersnot available
KeywordsIntercroppingHardwoodTree (set theory)Biomass (ecology)CropProductivityField experiment
DOInot available

Abstract

fetched live from OpenAlex

In Quebec, new strategies for improving the profitability of hardwood plantations have received little attention. Since 2000, field experiments have been established in southwestern Quebec in order to investigate the impact of introducing hybrid poplars (HP) in alternate rows with hardwood tree species. In 2004, one experimental site was converted into an intercropping system by adding a soybean crop between tree rows. Soybean was intercropped with three HP clones (NM-3729, DN-3308, TD-3230) spaced 6 (parcel A) or 8 m (parcel B) between tree rows. The growth of HP within the intercropping system was compared to sole tree treatment (disking of the alleys between tree rows). A positive effect of intercropped soybean on HP height increment was observed in parcel A, but there was no significant difference in parcel B. In parcel A, total soybean grain dry weight was significantly different between orientations with respect to tree row (east or west) and between HP clones. Total grain dry weight near the row was lower in the TD-3230 plots of parcel A, where relative photosynthetic photon flux density (Qp) was also lower. A decrease in both relative Qp and soil moisture in the upper 10 cm layer of the soil contributed to an important reduction of total grain dry weight, aboveground biomass and height of soybean plants at 2-3 versus 4-5 m from the HP rows. These results will provide vital information regarding the value of intercropping systems for improving the profitability of hardwood and HP plantations.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.032
GPT teacher head0.210
Teacher spread0.178 · 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
Published2015
Admission routes1
Has abstractyes

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