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Record W2899155208 · doi:10.1080/11956860.2018.1538591

Effects of spacing and herbaceous hydroseeding on water stress exposure and root development of poplars planted in soil-covered waste rock slopes

2018· article· en· W2899155208 on OpenAlexafffundvenue
Khadija Babi, Marie Guittonny, Guy R. Larocque, Bruno Bussière

Bibliographic record

VenueEcoscience · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHerbaceous plantRevegetationEnvironmental scienceBiomass (ecology)Competition (biology)SeedingAgronomyWoody plantPlant ecologyBiologyEcologyLand reclamation

Abstract

fetched live from OpenAlex

Root development is important to ensure tree survival in conditions of water stress. Despite their long-recognized role, little attention has been given to their development on waste rock slopes subject to rapid drainage. This study was conducted in an open-pit gold mine in a boreal forest. Its main objective was to establish a plantation design with a moderate level of competition for water resources on a waste rock slope. A hybrid poplar plantation was established in May 2013 on a soil-covered waste rock slope of 33%. The experimental design included three different poplar spacings: 1 × 1 m, 2 × 2 m without herbaceous seeding, 2 × 2 m with herbaceous seeding and 4 × 4 m. The poplars responded to increased competition resulting from closer spacing and herbaceous seeding by investing less energy in diameter and height growth. Poplar individuals that were subject to high levels of competition were able to acclimatize to water stress conditions by increasing root length density and specific root length and by reducing above-ground biomass. This study indicates that some clones of hybrid poplar showing phenotypic plasticity in the ratio of above- and belowground growth can be adapted for short-term revegetation of mine sites.

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)0.000
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 score0.153
Threshold uncertainty score0.119

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.181
Teacher spread0.174 · 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

Citations11
Published2018
Admission routes3
Has abstractyes

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