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Record W2735536468 · doi:10.5558/tfc2017-015

The NEBIE plot network: Background and experimental design

2017· article· en· W2735536468 on OpenAlexafffundvenueabout
F. Wayne Bell, Margo Shaw, Jennifer Dacosta, Steven G. Newmaster

Bibliographic record

VenueThe Forestry Chronicle · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversity of GuelphManitoba Environmental Industries AssociationOntario Forest Research Institute
FundersFPInnovationsOntario Innovation TrustCanadian Forest ServiceOntario Ministry of Natural Resources and ForestryNatural Resources CanadaMinistry of Natural ResourcesNatural Sciences and Engineering Research Council of CanadaU.S. Forest ServiceUniversity of Pittsburgh
KeywordsSilvicultureContext (archaeology)Forest managementForestryPlot (graphics)Scale (ratio)Environmental scienceGeographyEnvironmental resource managementEcologyCartographyBiologyMathematics

Abstract

fetched live from OpenAlex

The Intensive Management Science Partnership: NEBIE Plot Network is a stand-scale, multi-agency research project designed to compare the ecological effects of a range of silvicultural treatments in northern temperate and boreal forest regions of Ontario. The NEBIE plot network was established in 2001 with randomized complete block experiments installed at eight sites. The NEBIE acronym stands for Natural disturbance, and Extensive, Basic, Intensive, and Elite silviculture. Each NEBIE treatment was replicated at least three times at each site, using large experimental units (2-ha plots). The NEBIE plot network provides researchers with an opportunity to conduct long-term scientific studies at multiple scales and disciplines. The operational-scale treatment plots allow assessment of a variety of forest values in a context directly relevant to informing forest planning and management. In this paper, we document the experimental design and describe the sites and silviculture treatments. Information about sampling designs is provided, along with preliminary results, in a companion paper published in this edition of The Forestry Chronicle.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.996

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 studies0.0060.001
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 head0.068
GPT teacher head0.258
Teacher spread0.190 · 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.

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

Citations9
Published2017
Admission routes4
Has abstractyes

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