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Record W1534111581 · doi:10.1002/9780470057339.vnn147

Natural Disturbance Processes

2012· other· en· W1534111581 on OpenAlex
Craig DeLong, Philip J. Burton, Marten Geertsema

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEncyclopedia of Environmetrics · 2012
Typeother
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsDisturbance (geology)Natural (archaeology)EcologyVegetation (pathology)EcosystemEnvironmental scienceHabitatEcological successionLandslidePlant communityGeographyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Natural disturbance processes have a major influence on landscape and stand‐level patterns of vegetation age, size of vegetation patches, and horizontal and vertical structures of vegetation communities. A selective reorganization of the ecological community occurs after disturbances, resulting from a pulse of resources to which the new community responds. Through these influences, natural disturbance affects habitat availability and processes such as site productivity, hydrology, and animal movement. Natural disturbance processes as diverse as fire, biotic agents (such as grazing mammals, forest insects or fungal pathogens), volcanic eruptions, landslides, and flooding all affect an ecosystem in different ways and lead to unique vegetation patterns. Attempting to approximate natural disturbance processes with human‐caused disturbance is possible at some levels but remains fundamentally different, necessitating the retention of natural areas where natural disturbance can continue to play its important role in maintaining healthy ecosystems.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.335
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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