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Record W2887334221 · doi:10.3390/f9080471

Incorporating Insect and Wind Disturbances in a Natural Disturbance-Based Management Framework for the Boreal Forest

2018· article· en· W2887334221 on OpenAlex

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

VenueForests · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversité du Québec à MontréalMinistère des Ressources naturelles et des ForêtsUniversité LavalNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsSalvage loggingDisturbance (geology)WindthrowTaigaContext (archaeology)Forest managementEcologyBorealLoggingEnvironmental scienceEnvironmental resource managementEcosystemForest ecologyEcosystem managementNatural (archaeology)GeographyAgroforestryBiology

Abstract

fetched live from OpenAlex

Natural disturbances are fundamental to forest ecosystem dynamics and have been used for two decades to improve forest management, notably in the boreal forest. Initially based on fire regimes, there is now a need to extend the concept to include other types of disturbances as they can greatly contribute to forest dynamics in some regions of the boreal zone. Here we review the main descriptors—that is, the severity, specificity, spatial and temporal descriptors and legacies, of windthrow and spruce bud worm outbreak disturbance regimes in boreal forests—in order to facilitate incorporating them into a natural disturbance-based forest management framework. We also describe the biological legacies that are generated by these disturbances. Temporal and spatial descriptors characterising both disturbance types are generally variable in time and space. This makes them difficult to reproduce in an ecosystem management framework. However, severity and specificity descriptors may provide a template upon which policies for maintaining post harvesting and salvage logging biological legacies can be based. In a context in which management mainly targets mature and old-growth stages, integrating insect and wind disturbances in a management framework is an important goal, as these disturbances contribute to creating heterogeneity in mature and old-growth forest characteristics.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.934

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 studies0.0010.000
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 head0.020
GPT teacher head0.224
Teacher spread0.204 · 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