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Record W2029944565 · doi:10.1139/x03-128

Growth and nutrient uptake by birch and maple seedlings on soil with patchy or homogeneous distribution of organic matter

2003· article· en· W2029944565 on OpenAlexvenueno aff
Margret MI van Vuuren, Adrianna A. Muir, Colin M. Orians

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
FundersAndrew W. Mellon Foundation
KeywordsMapleNutrientOrganic matterAceraceaeBiomass (ecology)BotanyBetulaceaeSoil organic matterSoil waterAgronomyBiologyHorticultureChemistryEcology

Abstract

fetched live from OpenAlex

We compare responses to soil heterogeneity of red maple (Acer rubrum L.) and gray birch (Betula pop ulifolia Marsh.). Seedlings were grown with root systems split between two pots with soil: (i) without additional organic matter or nutrients ("no-addition" treatment), (ii) with additional organic matter and nutrients distributed evenly throughout the soil ("mixed" treatment), and (iii) with additional organic matter and nutrients concentrated in one pot ("patch" treatment). Compared with the no-addition treatment, mixed and patch treatments resulted in taller plants, and greater leaf and total plant dry masses for birch, while growth of maple was mostly unaffected. Birch root biomass was significantly increased in the organic patch. Specific root length of fine roots (<1 mm diameter) in the organic patch was twice as large for birch than for maple. Total plant biomass and N and P contents did not differ between mixed and patch treatments, possibly because the contrast between N and P concentrations between patch and non-patch soil was too small. In all treatments, birches took up more N than maples. In addition, the faster localized root growth and larger specific root length indicate a greater potential for birch than for maple to exploit heterogeneous soils.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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

Citations12
Published2003
Admission routes1
Has abstractyes

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