MétaCan
Menu
Back to cohort
Record W1776574918 · doi:10.2980/21-(3-4)-3645

Effect of soil mounding and mechanical weed control on hybrid poplar early growth and vole damage

2014· article· en· W1776574918 on OpenAlexafffundvenue
Annie DesRochers, Marie-Ève Sigouin

Bibliographic record

VenueEcoscience · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à Montréal
KeywordsWeed controlWeedAgronomyBiologyGrowing seasonForestryHorticultureGeography

Abstract

fetched live from OpenAlex

Abstract: This study compared growth of hybrid poplar in relation to soil mounding and mechanical weed control on a former agricultural site under boreal climate in order to assess optimal management scenarios. Height and basal diameter growth of 2 clones (Populus maximowiczii × P. balsamifera [915319] and P. × euramericana × P. maximowiczii [916401]) were evaluated after 3 growing seasons in 2 mounding (mounded, unmounded) and 4 weed control treatments (0: no weed control; 1, 2, and 3: 1, 2, or 3 passes of mechanical weed control). Net photosynthesis, stomatal conductance, soil temperature, and percent cover and height of competing vegetation were measured to explain the effects of soil mounding and weed control on tree growth. Two passes of weed control increased growth of trees by 23% in basal diameter and 12% in height, while mounding had no effect on growth. Both mounding and weed control reduced the cover of weedy vegetation, which was negatively correlated with growth of clone 915 319. Mounding did not increase mean soil temperatures in spring and even reduced them in the fall, while weed control had no effect on soil temperature. Finally, mounding significantly reduced the frequency and severity of damage (girdling) caused by voles during 2005–2006, especially for the plots that were not weeded.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.121

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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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 designBench or experimental
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

Citations13
Published2014
Admission routes3
Has abstractyes

Explore more

Same venueEcoscienceSame topicBioenergy crop production and managementFrench-language works237,207