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Record W7070725486

Pre- and post-forest management investigations of factors affecting sediment movement in riparian areas in Northwestern Ontario / by Darren J. McCormick. --

2004· dissertation· en· W7070725486 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2004
Typedissertation
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
Fundersnot available
KeywordsRiparian zoneHydrology (agriculture)SedimentMovement (music)Erosion
DOInot available

Abstract

fetched live from OpenAlex

The principal objectives of this study were to measure the impacts of timber harvesting on sediment transport rates (mineral and organic) associated with two clearcut areas in northwestern Ontario and to evaluate the sediment controlling effectiveness of 30 m wide riparian reserves that were prescribed in accordance with Ontario's Timber Management Guidelines For The Protection Of Fish Habitat Guidelines (OMNR, 1988).A goal of this study was to provide a practical means for assessing potential changes to sediment transport rates resulting from impacts of forest management activities in Ontario.Prior to road building and full tree logging with feller bunchers and grapple skidders, a sediment sampler was installed in each of 16 sub-catchments.Samplers were situated at a distance of either 0, 10, 20, or 30 m, measured into the reserve areas from the boundaries o f planned clearcuts.Mineral and organic sediment collected in the samplers were monitored for one year (late spring to late fall) before, and two years (late spring to late fall) after impacts, and data were standardized with the amount of precipitation that fell during the respective sampling year.Indices of change were calculated to quantify differences in mineral and organic collection rates for each sampler in the first or second post-impact year compared to those in the pre-impact year.Field and GIS data (including: sampler position in the reserve, sub-catchment area, distance from road, presence of surface runoff, occurrence of trees thrown by wind, crown closure, thickness of soil organic layers (LFH), terrain slope, and a topographic index (TI) derived from GIS data describing upstream contributing area and slope) were collected in an attempt to quantify the capacity of the reserve areas to impede (or encourage) sediment collection in each sampler.The results clearly demonstrate that sediment movement in riparian reserve areas does not increase universally following forest management.Sediment attenuation through the reserve areas was variable, indicating that factors in addition to the width of a filter strip can function to control the distance to which eroded sediment is transported.Catchment area was not related to the rates that sediment was collected in samplers.Sediment collection rates were higher in samplers located closer to the road; however, the results can not be used to support categorically the accepted model whereby areas closer to roads are subject to higher erosion rates than areas further away from roads.The occurrence of surface runoff and windthrow, especially when combined, were predominant factors that contributed to increased sediment collection rates in samplers.The amount of crown closure and the thickness of LFH layers influenced rates of erosion, but the magnitudes of their influences were marginal compared to those of flow and windthrow.Steeper slopes did not consistently generate higher sediment transport rates, but the evaluation of the

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)0.000
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 score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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
Published2004
Admission routes1
Has abstractno

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