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Record W2163388897 · doi:10.1657/1938-4246-43.3.389

The Effects of Fertilization and Herbivory on the Phenology of the Understory Vegetation of the Boreal Forest in Northwestern Canada

2011· article· en· W2163388897 on OpenAlexafffundabout
Jennie R. McLaren, Lisa Ann DeSandoli, Roy Turkington

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaArctic Institute of North America
KeywordsPhenologyFencingUnderstoryFertilizerTaigaAgronomyBorealEnvironmental scienceBiologyEcology

Abstract

fetched live from OpenAlex

Environmental conditions associated with climate change, such as earlier snowmelt, warmer spring temperatures, and increased soil mineralization, have resulted in shifts in the timing of plant phenological events. We assessed the effects of fertilizer and herbivory on the phenology of 7 plant species from a boreal forest understory using long-term experimental plots in the southwest Yukon. Fertilizer and fencing treatments were initiated in 1990, and discontinued in half of each plot in 2000. There were few effects on phenology of either fertilizer or fencing. In some species, fertilizer affected the final phenological stage reached, but the presence and direction of the effect was species-dependent. Epilobium angustifolium was the only species where the timing of phenological stages responded to fertilization; early phenological stages were advanced with fertilizer. First leaf expansion for Arctostaphylos uva-ursi occurred earlier in fenced plots, although this effect disappeared in plots where the fencing treatment had been discontinued. We conclude that previously observed changes in species abundance with fertilizer treatments are likely not caused by changes in the phenology of these species, and are more likely due to fertilization imposed changes in vegetative growth.

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)0.000
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 score0.421
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.039
GPT teacher head0.252
Teacher spread0.213 · 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

Citations8
Published2011
Admission routes3
Has abstractyes

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