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Record W4391262743 · doi:10.1007/s40725-023-00208-y

Enhancing Tree Performance Through Species Mixing: Review of a Quarter-Century of TreeDivNet Experiments Reveals Research Gaps and Practical Insights

2024· review· en· W4391262743 on OpenAlexafffund
Leen Depauw, Emiel De Lombaerde, Els Dhiedt, Haben Blondeel, Luis Abdala‐Roberts, Harald Auge, Nadia Barsoum, Jürgen Bauhus, Chengjin Chu, Abebe Damtew, Nico Eisenhauer, Marina V. Fagundes, Gislene Ganade, Benoît Gendreau-Berthiaume, Douglas L. Godbold, Dominique Gravel, Joannès Guillemot, Peter Hajek, Andy Hector, Bruno Hérault, Hervé Jactel, Julia Koricheva, Holger Kreft, Xiaojuan Liu, Simone Mereu, Christian Messier, Bart Muys, Charles A. Nock, Alain Paquette, John D. Parker, William C. Parker, Gustavo B. Paterno, Michael P. Perring, Quentin Ponette, Catherine Potvin, Peter B. Reich, Boris Rewald, Michael Scherer‐Lorenzen, Florian Schnabel, Rita Sousa‐Silva, Martin Weih, Delphine Clara Zemp, Kris Verheyen, Lander Baeten

Bibliographic record

VenueCurrent Forestry Reports · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsMcGill UniversityMinistry of Natural Resources and ForestryUniversité de SherbrookeUniversité du Québec à MontréalUniversity of AlbertaUniversité du Québec en Outaouais
FundersFondation BNP ParibasBNP Paribas CardifBelgian Federal Science Policy OfficeSvenska Forskningsrådet FormasAustrian Science FundAgence Nationale de la RechercheCanada Research ChairsFundação de Amparo à Pesquisa do Estado de São PauloDeutsche ForschungsgemeinschaftNational Science Foundation
KeywordsAfforestationProductivityDiversity (politics)EcologyEnvironmental resource managementTree (set theory)BiodiversityAgroforestryMonocultureForest managementGeographyBiologyEnvironmental sciencePolitical scienceEconomics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.122
GPT teacher head0.417
Teacher spread0.295 · 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.

Study designSystematic review
DomainMethods
GenreReview

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

Citations50
Published2024
Admission routes2
Has abstractno

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