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Record W2000144894 · doi:10.1139/x10-141

Nitrate stimulates root suckering in trembling aspen (Populus tremuloides)

2010· article· en· W2000144894 on OpenAlexafffundvenue
Simon M. Landhäusser, Xianchong Wan, Victor J. Lieffers, Pak S. Chow

Bibliographic record

VenueCanadian Journal of Forest Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta-Pacific Forest Industries
KeywordsSuckerNutrientNitrificationChemistryBotanySalicaceaeNitrateAgronomyAnimal scienceHorticultureWoody plantNitrogenBiology

Abstract

fetched live from OpenAlex

In a greenhouse experiment, we tested whether the initiation, density, and growth of trembling aspen ( Populus tremuloides Michx.) root suckers are related to postdisturbance soil nutrient availability. After decapitation of functional 2-year-old aspen root systems, nutrient solutions adjusted for various concentrations and forms of mineral N, different concentrations of Ca 2+ , K + , or PO 4 3– , and different pH were applied to the roots and their suckering response was assessed after 35 days. Root systems treated with NO 3 – at concentrations of 1.5 and 7.5 mmol/L produced nearly double the sucker density compared with an unfertilized control, while fertilizing with N in the form of NH 4 + did not affect sucker numbers, regardless of concentrations. The best growth of suckers was achieved with a mixture of 15 mmol/L NO 3 – + NH 4 + whereas the lowest growth was observed with 15 mmol/L NH 4 + . Neither Ca 2+ , K + , and PO 4 3– nor the pH tested in this study impacted sucker density or growth. This has implications for understanding the impacts of disturbance on forest succession and the subsequent regeneration of aspen stands. The results suggest that the amount of nitrification, depending on the type and severity of disturbances, will influence the regeneration density of aspen.

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 score0.799
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.314
Teacher spread0.265 · 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

Citations14
Published2010
Admission routes3
Has abstractyes

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