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Record W2918886426 · doi:10.3188/szf.2019.0069

Marteloscopes au service de la sylviculture proche de la nature

2019· article· en· W2918886426 on OpenAlexaff
Pascal Junod

Bibliographic record

VenueSchweizerische Zeitschrift fur Forstwesen · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsMinistère des Ressources naturelles et des Forêts
Fundersnot available
KeywordsSilvicultureForest managementService (business)SustainabilityBusinessSustainable forest managementEnvironmental resource managementCompetition (biology)AgroforestryForestryGeographyEcologyEnvironmental scienceMarketing

Abstract

fetched live from OpenAlex

Marteloscopes in the service of close-to-nature silviculture In Switzerland, close-to-nature silviculture is recognised as strategically important. It is part of the management principles prescribed in Article 20 of the Federal Act on Forests. The implementation of this type of silviculture is based on a holistic understanding of the forest, which is considered both as a complex habitat and as a multifunctional production system. Marking is the core activity of forest management. It is an integrative decision-making act at the interface between planning and timber harvesting. Since 2011, the silvicultural department in Lyss has been using marteloscopes for training purposes. They are valued as didactic tools and enable practice of a forest management that is carried out in accordance with the overarching legal objectives of sustainability, naturalness and multifunctionality. Thanks to the georeferencing of the trees and the recording of their characteristics, it is possible to compare the various marking proposals visually and quantitatively. The marteloscope exercises, carried out in a spirit of healthy competition, are one of the pillars of the training of forest professionals.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.008

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 head0.256
Teacher spread0.251 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2019
Admission routes1
Has abstractyes

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