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Record W2030441608 · doi:10.1139/b00-031

The effects of recreation disturbance on subalpine seed banks in the Rocky Mountains of Montana

2000· article· en· W2030441608 on OpenAlexvenueno aff
Catherine A. Zabinski, Todd Wojtowicz, David Cole

Bibliographic record

VenueCanadian Journal of Botany · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotany, Ecology, and Taxonomy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRevegetationPropaguleVegetation (pathology)Environmental scienceDisturbance (geology)Organic matterSoil seed bankEcosystemBulk densityEcologySeedlingAgronomyBiologySoil waterEcological successionSoil science

Abstract

fetched live from OpenAlex

We investigated the soil seed bank in a subalpine ecosystem with patchy disturbance from camping. Soil cores were collected from three site types, heavily impacted, lightly impacted, and undisturbed, that differed in area of bare ground and depth of surface organic matter. We hypothesized that the density and composition of the seed bank would vary with depth of surface organic matter and distance from established vegetation. Seedling emergence was determined in the greenhouse. Seed density was significantly lower on disturbed sites, averaging 441 seeds/m 2 on heavily impacted sites, 1495 seeds/m 2 on lightly impacted sites, and 4188 seeds/m 2 on undisturbed sites. Seed density declined exponentially with distance from established vegetation and increased with depth of surface organic matter. The number of species present did not vary across site types, but 10 species that occurred on lightly impacted and undisturbed sites were not present on heavily impacted sites. We concluded that disturbance that causes removal of surface organic matter can affect natural revegetation by lowering the density of propagules and affecting the species represented in the seed bank.Key words: seed bank, subalpine, patchy disturbance, recreation impacts.

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 score0.334
Threshold uncertainty score0.676

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 studies0.0000.000
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 head0.010
GPT teacher head0.189
Teacher spread0.179 · 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

Citations36
Published2000
Admission routes1
Has abstractyes

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