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Record W2038355302 · doi:10.4141/s06-058

Impact of chipping residues and its leachate on the initiation and growth of aspen root suckers

2007· article· en· W2038355302 on OpenAlexaffvenue
Simon M. Landhäusser, Victor J. Lieffers, Pak S. Chow

Bibliographic record

VenueCanadian Journal of Soil Science · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSuckerResidue (chemistry)Bark (sound)Root systemChemistryBotanyHorticultureLeachateAgronomyBiologyAnatomyEnvironmental chemistryEcology

Abstract

fetched live from OpenAlex

Field chipping operations often disperse chipping residues of bark and branches in layers or piles in cut-overs. We tested the effects of these residues on the root sucker regeneration of aspen (Populus tremuloides Michx.), using root systems established in large pots. After decapitation of the stems, the root systems were covered with a 4 cm thick layer of chipping residues. Other root systems were treated with an extract of the water-soluble compounds leached from a similar amount of residues while others were left untreated as controls; all three treatments were left to sucker for 7 wk. There were no differences in the number of root suckers that were initiated on the aspen root system among the three treatments. There were, however, significantly lower numbers of suckers that emerged through the chipping residue and their emergence was delayed relative to the other treatments. The delay in emergence and the reduction in sucker numbers were likely a result of the residues acting as a physical barrier. After 7 wk, concentrations of water-soluble phenolic compounds, sugars, and carbon from the chipping residues were elevated in the soil; however, they appeared to be below a level that affects sucker emergence and development. Key words: Allelochemistry, leachate, physical barrier, Populus tremuloides, regeneration

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)0.002
metaresearch head score (Gemma)0.001
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 score0.024
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.022
GPT teacher head0.246
Teacher spread0.225 · 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

Citations17
Published2007
Admission routes2
Has abstractyes

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