MétaCan
Menu
Back to cohort
Record W60203721 · doi:10.1093/jof/103.1.47

Digital Forestry: A White Paper

2005· article· en· W60203721 on OpenAlexafffund
Guang Zhao, Guofan Shao, Keith M. Reynolds, Michael C. Wimberly, T. T. Warner, John W. Moser, Keith Rennolls, Steen Magnussen, Michael Köhl, Hans-Erik Anderson, Guillermo Mendoza, Andreas Huth, Liangjun Zhang, James A. Brey, Yujun Sun, Ronghua Ye, Brett Martin, Fengri Li

Bibliographic record

VenueJournal of Forestry · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsCanadian Forest Service
FundersPacific Northwest Research StationU.S. Forest ServiceUniversität HamburgPurdue UniversityChinese Academy of SciencesNational Natural Science Foundation of ChinaU.S. Department of AgricultureChinese Academy of ForestryUniversity of Illinois at Urbana-ChampaignNortheast Forestry UniversityWest Virginia UniversityCanadian Forest ServiceDivision of Mathematical SciencesInstitute of Applied Ecology, Chinese Academy of SciencesUniversity of GreenwichNational Science FoundationUniversity of WashingtonDepartment of Forestry and Natural Resources, Purdue UniversitySyracuse University
KeywordsForestrySustainable forest managementCommunity forestryForest managementHost (biology)Sustainable developmentForest ecologyEnvironmental resource managementDigital ecosystemBusinessEcosystemComputer scienceGeographyPolitical scienceEcologyEnvironmental scienceKnowledge management

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.069
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0070.004
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0690.023

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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations28
Published2005
Admission routes2
Has abstractno

Explore more

Same venueJournal of ForestrySame topicRemote Sensing and LiDAR ApplicationsFrench-language works237,207