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Record W1974978908 · doi:10.1139/s03-029

Expanding the forest management framework in the province of Alberta to include landscape-based research

2003· article· en· W1974978908 on OpenAlexfundvenueaboutno aff
Daniel W. Smith, Jonathan S. Russell, J. M. Burke, Ellie E. Prepas

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsnot available
FundersGovernment of Canada
KeywordsForest managementTaigaRiparian zoneEnvironmental resource managementDisturbance (geology)WatershedSustainable forest managementCertified woodBusinessGeographyForestryEcologyEnvironmental scienceHabitat

Abstract

fetched live from OpenAlex

The Forest Watershed and Riparian Disturbance (FORWARD) project was initiated in the western Canadian province of Alberta, the site of some of the most intensive forest activities on the Boreal Plain subregion of the Canadian Boreal Forest, including forestry, oil and gas extraction, and mining. Forest management falls primarily within provincial/territorial jurisdiction in Canada; therefore, we outline the processes for forest management planning and practices at the provincial level. In Alberta, the Ministry of Sustainable Resource Development allocates tenure of forested areas to forest products companies via forest management agreements (FMAs). Conditions for company activities in the FMA area are cooperatively designed and documented in detailed forest management plans. We examine Alberta Government policies with respect to watershed management in forested areas, with an emphasis on aquatic ecosystems. Further, needs for changes to current government policy and practices are discussed, with recognition of the pressures that exist on the regulatory effort. Key words: forest management, forest harvest, regulation, policy, legislation.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.101

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.022
GPT teacher head0.240
Teacher spread0.219 · 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.

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

Citations16
Published2003
Admission routes3
Has abstractyes

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