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Record W2022806918 · doi:10.5558/tfc79054-1

Ice storm damage: Effects of competition and fertilization on near-ground vegetation

2003· article· en· W2022806918 on OpenAlexafffundvenueabout
R. A. Lautenschlager, John Pedlar, Cathy Nielsen

Bibliographic record

VenueThe Forestry Chronicle · 2003
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsMinistry of Natural Resources and ForestryCanadian Forest ServiceOntario Forest Research Institute
FundersGovernment of CanadaGovernment of Ontario
KeywordsDeciduousVegetation (pathology)Species richnessAgronomyShrubLimeHerbaceous plantPlant coverEnvironmental scienceForestryEcologyBiologyGeography

Abstract

fetched live from OpenAlex

Increasing ice damage to tree canopies led to increased cover of near-ground deciduous tree species, herbaceous species, and total vegetative cover but reduced fern cover in managed sugar maple stands in southeastern Ontario. Near-ground vegetation did not respond to the addition of fertilizers [2000 kg/ha of dolomitic lime, 200 kg/ha of both phosphorus (P) and potassium (K), or both lime and P and K]. Vegetation management with glyphosate in these stands reduced near-ground deciduous tree cover 86%, while grass and sedge cover were reduced 69%, and shrub cover was reduced 98% two years after treatment. Although species richness was initially reduced by vegetation management, species richness levels on treated plots were comparable to, or higher than, those on untreated plots by two years after treatment. Key words: Acer saccharum, glyphosate, plant cover

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.125
Threshold uncertainty score0.249

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2003
Admission routes4
Has abstractyes

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