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Record W265925576 · doi:10.1139/s03-037

Ecologically-based forest planning and management for aquatic ecosystems in the Duck Mountains, Manitoba

2003· article· en· W265925576 on OpenAlexvenueaboutno aff
Margaret Donnelly

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRiparian zoneWatershedWatershed managementForest managementEnvironmental resource managementEcosystem managementDisturbance (geology)Forest ecologySustainable managementRiparian forestAquatic ecosystemEnvironmental scienceEcosystemGeographyAgroforestryEcologySustainabilityHabitatComputer science

Abstract

fetched live from OpenAlex

Current forest management guidelines require the extensive use of stand level approaches to minimize impacts on aquatic ecosystems. As forest management practices in Canada evolve from a sustained yield, timber-based focus to a more sustainable approach, the need has been recognized for the addition of landscape or watershed-based management strategies to address the cumulative effects of management practices on aquatic systems. Additional concerns expressed by regulatory agencies have resulted in the need to restrict cumulative harvest rates and monitor harvest levels for a forest management operation in Manitoba. Present riparian management practices and watershed analysis procedures are reviewed for the operations of Louisiana-Pacific Canada in Manitoba. Requirements are outlined for multi-scale indicators and research to support the development of a watershed-based planning approach for use in forest management planning and monitoring. The development of this watershed-based approach is linked to the Forest Watershed and Riparian Disturbance (FORWARD) project. Key words: riparian management, watershed management, natural disturbance, forest harvest.

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)0.001
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 score0.256
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.196
Teacher spread0.187 · 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

Citations4
Published2003
Admission routes2
Has abstractyes

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