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Record W2157728534 · doi:10.5558/tfc81717-5

An adaptive management process for forest soil conservation

2005· article· en· W2157728534 on OpenAlexaffvenueabout
Michael P. Curran, Douglas G. Maynard, Ronald L. Heninger, Thomas A. Terry, Steven W. Howes, Douglas M. Stone, Thomas Niemann, Richard E. Miller, Robert F. Powers

Bibliographic record

VenueThe Forestry Chronicle · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceGovernment of British Columbia
Fundersnot available
KeywordsDisturbance (geology)Environmental scienceEnvironmental resource managementSoil functionsProcess (computing)Adaptive managementVulnerability (computing)Forest managementCertificationSoil waterComputer scienceAgroforestrySoil scienceSoil biodiversitySoil fertility

Abstract

fetched live from OpenAlex

Soil disturbance guidelines should be based on comparable disturbance categories adapted to specific local soil conditions, validated by monitoring and research. Guidelines, standards, and practices should be continually improved based on an adaptive management process, which is presented in this paper. Core components of this process include: reliable monitoring protocols for assessing and comparing soil disturbance for operations, certification and sustainability protocols; effective methods to predict the vulnerability of specific soils to disturbance and related mitigative measures; and, quantitative research to build a database that documents the practical consequences of soil disturbance for tree growth and soil functions. Key words: soil disturbance; soil compaction; rutting; monitoring (implementation, effectiveness, and validation); criteria and indicators; Montreal Process

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)0.024
metaresearch head score (Gemma)0.043
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: none
Teacher disagreement score0.024
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.031
GPT teacher head0.252
Teacher spread0.221 · 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

Citations22
Published2005
Admission routes3
Has abstractyes

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