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Record W3215877573 · doi:10.1093/forestry/cpab047

The changing culture of silviculture

2021· article· en· W3215877573 on OpenAlexafffund
Alexis Achim, Guillaume Moreau, Nicholas C. Coops, Jodi Axelson, Julie Barrette, Steve Bédard, Kenneth Earl Byrne, John P. Caspersen, Adam Dick, Loïc D’Orangeville, Guillaume Drolet, Bianca N.I. Eskelson, Cosmin N. Filipescu, Maude Flamand-Hubert, Tristan R.H. Goodbody, Verena C. Griess, Shannon Hagerman, Kevin Keys, Benoît Lafleur, Miguel Montoro Girona, Dave Morris, Charles A. Nock, Bradley D. Pinno, Patricia Raymond, Vincent Roy, Robert Schneider, Michel Soucy, Bruce Stewart, Jean‐Daniel Sylvain, Anthony R. Taylor, Évelyne Thiffault, Nelson Thiffault, Udaya Vepakomma, Joanne C. White

Bibliographic record

VenueForestry An International Journal of Forest Research · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversité du Québec à RimouskiUniversity of AlbertaMinistry of Natural Resources and ForestryMinistère des Ressources naturelles et des ForêtsFPInnovationsUniversité de MonctonMinistry of ForestsUniversity of TorontoUniversité du Québec en Abitibi-TémiscamingueUniversity of New BrunswickCanadian Forest ServiceUniversity of British ColumbiaNatural Resources CanadaUniversité Laval
FundersNatural Resources CanadaFPInnovationsNatural Sciences and Engineering Research Council of CanadaEidgenössische Technische Hochschule Zürich
KeywordsSilvicultureKey (lock)Diversification (marketing strategy)Forest managementEnvironmental resource managementSoftware deploymentEcosystem servicesProcess (computing)Computer scienceSustainable forest managementForest ecologyBusinessEcosystemEcologyAgroforestryEnvironmental scienceMarketing

Abstract

fetched live from OpenAlex

Abstract Changing climates are altering the structural and functional components of forest ecosystems at an unprecedented rate. Simultaneously, we are seeing a diversification of public expectations on the broader sustainable use of forest resources beyond timber production. As a result, the science and art of silviculture needs to adapt to these changing realities. In this piece, we argue that silviculturists are gradually shifting from the application of empirically derived silvicultural scenarios to new sets of approaches, methods and practices, a process that calls for broadening our conception of silviculture as a scientific discipline. We propose a holistic view of silviculture revolving around three key themes: observe, anticipate and adapt. In observe, we present how recent advances in remote sensing now enable silviculturists to observe forest structural, compositional and functional attributes in near-real-time, which in turn facilitates the deployment of efficient, targeted silvicultural measures in practice that are adapted to rapidly changing constraints. In anticipate, we highlight the importance of developing state-of-the-art models designed to take into account the effects of changing environmental conditions on forest growth and dynamics. In adapt, we discuss the need to provide spatially explicit guidance for the implementation of adaptive silvicultural actions that are efficient, cost-effective and socially acceptable. We conclude by presenting key steps towards the development of new tools and practical knowledge that will ensure meeting societal demands in rapidly changing environmental conditions. We classify these actions into three main categories: re-examining existing silvicultural trials to identify key stand attributes associated with the resistance and resilience of forests to multiple stressors, developing technological workflows and infrastructures to allow for continuous forest inventory updating frameworks, and implementing bold, innovative silvicultural trials in consultation with the relevant communities where a range of adaptive silvicultural strategies are tested. In this holistic perspective, silviculture can be defined as the science of observing forest condition and anticipating its development to apply tending and regeneration treatments adapted to a multiplicity of desired outcomes in rapidly changing realities.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.135
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.338
Teacher spread0.317 · 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

Citations134
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueForestry An International Journal of Forest ResearchSame topicFire effects on ecosystemsFrench-language works237,207