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Record W2155978916

The Bowron River watershed: a landscape level assessment of post-beetle change in stream riparian function.

2009· article· en· W2155978916 on OpenAlexaff
Lisa Nordin, D. Maloney, John F. Rex, Phillip Krauskopf, Peter Tschaplinski, Dan Hogan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsRiparian zoneRiparian bufferEnvironmental scienceSTREAMSWatershedHydrology (agriculture)Riparian forestClearcuttingLoggingDrainage basinStructural basinGeographyEcologyHabitatGeologyForestryBiologyCartography
DOInot available

Abstract

fetched live from OpenAlex

Streams and riparian areas in the Bowron River watershed were assessed using the Routine Riparian Effectiveness Evaluation (RREE) to determine their level of ecological function 20-30 years after accelerated harvest activity. The RREE is a procedure that includes both stream and riparian indicators to assess the health and condition of a stream reach. Sites in heavily harvested sub-basins had lower overall evaluation scores than reference sites, mainly because of high failure rates of riparian indicators. Larger streams located lower in the sub-basin appeared to score slightly better than those in the upper basin and this is likely due to a larger riparian buffer at lower basin sites. A regeneration time of 20-30 years after clearcutting was determined to be insufficient for the recovery of riparian indicators to pre-harvest conditions. Variation among sites with respect to stream indicators appeared higher within the harvested and reference groups than between them, indicating that harvesting effects have diminished and natural variability is a stronger governing factor. The within-group variability was explained in part by differences in slope, channel width, coupling and soil erodibility. Recommendations for salvage logging best management practices are given based on observations of recovery from past harvesting activities and site specific characteristics.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.247
Teacher spread0.228 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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