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Record W3092414574 · doi:10.1007/s11629-019-5890-0

Combined forest and soil management after a catastrophic event

2020· article· en· W3092414574 on OpenAlexaff
Augusto Zanella, Jean‐François Ponge, Anna Andreetta, Michaël Aubert, Nicolas Bernier, Eleonora Bonifacio, Karine Bonneval, Cristian Bolzonella, Oleg Chertov, Edoardo A.C. Costantini, Maria De Nobili, Silvia Fusaro, Raffaello Giannini, Pascal Junod, Klaus Katzensteiner, Jolantha Kwiatkowsk-Malina, Roberto Menardi, Lingzi Mo, Safwan Mohammed, Annik Schnitzler, Adriano Sofo, Dylan Tatti, Herbert Hager

Bibliographic record

VenueJournal of Mountain Science · 2020
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsMinistère des Ressources naturelles et des Forêts
Fundersnot available
KeywordsNatural (archaeology)Interpretation (philosophy)NothingAction (physics)AgricultureHistoryPolitical scienceEnvironmental resource managementEnvironmental scienceArchaeologyEpistemologyComputer science

Abstract

fetched live from OpenAlex

At the end of October 2018, a storm of unprecedented strength severely damaged the forests of the eastern sector of the Italian Alps. The affected forest area covers 42,500 ha. The president of one of the damaged regions asked for help from the University of Padua. After eight months of discussion, the authors of this article wrote a consensus text. The sometimes asper debate brought to light some crucial aspects: 1) even experienced specialists may have various opinions based on scientific knowledge that lead to conflicting proposals for action. For some of them there is evidence that to restore a destroyed natural environment it is more judicious to do nothing; 2) the soil corresponds to a living structure and every ecosystem's management should be based on it; 3) faced with a catastrophe, people and politicians find themselves unarmed, also because they rarely have the scientific background to understand natural processes. Yet politicians are the only persons who make the key decisions that drive the economy in play and therefore determine the near future of our planet. This article is an attempt to respond directly to a governor with a degree in animal production science, who formally and prudently asked a university department called "Land, Environment, Agriculture and Forestry" for help before taking decisions; 4) the authors also propose an artistic interpretation of facts (uncontrolled storm) and conclusions (listen to the soil). Briefly, the authors identify the soil as an indispensable source for the renewal of the destroyed forest, give indications on how to prepare a map of the soils of the damaged region, and suggest to anchor on this soil map a series of silvicultural and soil management actions that will promote the soil conservation and the faster recovery of the natural dynamic stability and resilience. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available for this article at 10.1007/s11629-019-5890-0 and is accessible for authorized users.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.211
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations10
Published2020
Admission routes1
Has abstractyes

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