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Record W2047196324 · doi:10.1139/x02-100

Interspecific competition for nitrogen between early successional species and planted white spruce and jack pine seedlings

2002· article· en· W2047196324 on OpenAlexfundvenueno aff
R.D. Hangs, J. Diane Knight, Ken CJ Van Rees

Bibliographic record

VenueCanadian Journal of Forest Research · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaForskningsrådet om Hälsa, Arbetsliv och Välfärd
KeywordsMonocultureInterspecific competitionBiologyBotanyTaigaCompetition (biology)FertilizerAgronomyEcology

Abstract

fetched live from OpenAlex

Relatively little is known about belowground competition for nitrogen (N) between boreal forest species. The objective of this study was to compare the relative competitiveness of three early successional boreal forest species, trembling aspen (Populus tremuloides Michx.), fireweed (Epilobium angustifolium L.), and calamagrostis (Calamagrostis canadensis (Michx.) Beauv.), on 15 N uptake and the growth response by containerized white spruce (Picea glauca (Moench) Voss) and jack pine (Pinus banksiana Lamb.) seedlings. Three densities (0, 2, and 6 plants/pot) of each competitor species were interplanted with individual seedlings in pots with 15 N-labelled fertilizer and grown for 3 months. The conifer seedlings had the greatest fertilizer 15 N uptake (129.5 and 82.3 µg/m 2 root for white spruce and jack pine, respectively) when grown in monoculture and the lowest uptake when interplanted with six competitor plants (9.2 and 8.6 µg/m 2 root for white spruce and jack pine, respectively). Whether grown in monoculture or with conifer seedlings, calamagrostis took up the greatest amount of fertilizer 15 N. Vegetation management practices that reduce the establishment of this grass species in the field should benefit N uptake and growth of outplanted white spruce and jack pine seedlings.

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 score0.028
Threshold uncertainty score0.987

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)0.0010.000

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 head0.065
GPT teacher head0.274
Teacher spread0.209 · 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

Citations28
Published2002
Admission routes2
Has abstractyes

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