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Record W2530173553

Effects of introduced moose (Alces alces) on vegetation composition, nutrient dynamics, and decomposition rates in boreal forest ecosystems in Newfoundland, Canada

2016· dissertation· en· W2530173553 on OpenAlexaboutno aff
Nichola Ellis

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2016
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsExclosureLitterEcosystemTaigaEnvironmental scienceBiomass (ecology)EcologyHerbivoreNutrientDisturbance (geology)BorealBiology
DOInot available

Abstract

fetched live from OpenAlex

Globally, consumers affect ecosystem processes including nutrient dynamics. Herbivores have been known to slow nutrient flow in boreal forest ecosystems. I examined the effects of introduced moose on disturbed forests of Newfoundland, Canada by conducting a field experiment during August - November 2014 in 20 paired moose exclosure-control plots. I tested whether moose browsing directly and indirectly affected forests by measuring plant species composition, litter quality and quantity, soil quality, and decomposition rates in areas moose exclosure-control plots. I analyzed moose effects using linear mixed effects models and found evidence indicating that moose reduce plant height and litter biomass affecting the availability of carbon, nitrogen, and phosphorus. However, plant diversity, soil quality, and litter decomposition did not differ between moose exclosures and controls. Moose in Newfoundland directly influence plant regeneration and litter biomass while indirect effects on soil ecosystems may be limited by time, disturbance, and climate.

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 score0.462
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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