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Record W1979127847 · doi:10.1614/ws-d-12-00095.1

Regeneration of Canada Thistle (<i>Cirsium arvense</i>) from Intact Roots and Root Fragments at Different Soil Depths

2013· article· en· W1979127847 on OpenAlexaboutno aff
Mette Goul Thomsen, Lars-Olav Brandsæter, Haldor Fykse

Bibliographic record

VenueWeed Science · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsnot available
FundersNorges Forskningsråd
KeywordsThistleCirsium arvenseShootBiomass (ecology)QuadratAgronomyBiologyBotanyHorticultureShrub

Abstract

fetched live from OpenAlex

In the present field study, the capability of Canada thistle to develop shoots from intact roots and root fragments at different soil depths was studied. The experiments were performed on four sites with high-density Canada thistle, with three or four replications per treatment. At each site, the soil in the plots was removed layer by layer (to 30 or 40 cm, depending on the site), within a 1 by 1-m quadrat, and spread out on a plastic sheet. All roots and other plant parts were removed, and the soil was either replaced without any root material (two sites), or the roots of the thistles were cut into 10-cm-long fragments and replaced into the source holes (two sites). The measured variables were shoot number and biomass. The number of shoots of Canada thistle decreased with increasing depth (P < 0.001) and increased with time. Additionally, the two factors interacted (P < 0.001) such that shoot development was slower from greater depths. Roots from ≤ 20 cm depth produced higher biomasses than did roots from below 20 cm depth. Replacement of root fragments did not affect the amount of biomass produced. It was concluded that the intact root system contributed considerably more to the total biomass produced by Canada thistle than did the root fragments in the upper soil layers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), 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
Published2013
Admission routes1
Has abstractyes

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