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Record W2482897409 · doi:10.1139/cjb-2016-0015

Can graminoids used for mine tailings revegetation improve substrate structure?

2016· article· en· W2482897409 on OpenAlexaffvenue
Marie Guittonny‐Larchevêque, Yasmine Meddeb, Dominique Barrette

Bibliographic record

VenueBotany · 2016
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
FundersUniversité de Montpellier
KeywordsGraminoidTailingsRevegetationEnvironmental scienceBiomass (ecology)AgronomyPeatBulk densityLand reclamationSoil waterForbSoil scienceBiologyEcologyGrasslandMaterials science

Abstract

fetched live from OpenAlex

The seeding of agronomic graminoid species that are tolerant to the compacted and low aeration conditions associated with mine tailings allows for rapid cover of mine waste, which in turn controls erosion. These graminoids can be used as primer-species on mine tailings to improve the rooting of other plant species, which may not tolerate soil compaction and low aeration. Tailings colonization by graminoid roots could improve ecological filters such as low air-filled porosity and elevated bulk density. The effect of above- and below-ground development of graminoid species used for hay-field seeding on the macroporosity and density of gold mine tailings was studied under controlled conditions as well as in situ. All of the graminoid species tested improved the macroporosity of the tailings after only 2 months of growth under greenhouse conditions, but had no effect on the density of the tailings. The perennial Bromus inermis Leyss. was most efficient in improving the macroporosity of tailings, having greater root diameter, biomass, and volume. The annual Avena sativa L. also produced high root biomass and length, which improved the macroporosity of the tailings. However, under field conditions, graminoids had low cover and no effect on macroporosity, which highlights that their growth should be improved to make them usefull as primer-plants.

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 score0.047
Threshold uncertainty score0.380

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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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

Citations17
Published2016
Admission routes2
Has abstractyes

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