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Record W2704555444 · doi:10.14288/1.0314334

Evaluating Current approaches to Riparian Management in British Columbia

2017· article· en· W2704555444 on OpenAlexaffabout
Stephanie von Loessl

Bibliographic record

VenuecIRcle (University of British Columbia) · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRiparian zoneCurrent (fluid)Environmental resource managementGeographyHistoryEnvironmental scienceGeologyOceanographyEcology

Abstract

fetched live from OpenAlex

Riparian forests are tightly linked to freshwater streams making these ecosystems vulnerable to alterations that occur from timber harvesting. In order to protect stream resources, fixed-width buffers were implemented in BC in the Riparian Management Area Guidebook (1995) under the Forest Practices Code (FPC). It has been found that these buffers have not been extensively subjected to scientific validation and commonly under-protect site-specific conditions. The Forest and Range Practices Act (FRPA) was introduced in 2004 in order to deregulate management planning for licensees. However, FRPA uses result-based objectives which are difficult to measure, making it challenging for foresters to comply with the legislation, causing foresters to default to the Riparian Management Area Guidebook (1995). I used a survey to assess and quantify common riparian management protocols in BC. The survey found that foresters commonly follow the Guidebook, even though it is felt that the Guidebook is outdated and lacks adequate protection for small streams and site-specific conditions. Thus, foresters are found to deviate from the fixed-width buffers to manage for site-specific conditions, such as stands vulnerable to windthrow, sensitive fish habitat and other hydrologically sensitive areas.

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)0.004
metaresearch head score (Gemma)0.012
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.710

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0030.002
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.230
Teacher spread0.175 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

Explore more

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