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Record W2338926617 · doi:10.14288/1.0087786

Detecting the effects of forestry on lacustrine sedimentation on the West Coast of Vancouver Island, British Columbia

2009· article· en· W2338926617 on OpenAlexaboutno aff
Emmanuelle Arnaud

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsnot available
Fundersnot available
KeywordsWest coastSedimentationForestryArchaeologyGeologyGeographyPhysical geographyOceanographyGeomorphologySediment

Abstract

fetched live from OpenAlex

Enhanced sediment yield associated with forestry activity is well documented. While some studies have focused on assessing the increase in sediment concentrations of streams, the extent to which sediment is transmitted down valley to storage areas such as lakes remains to be established. There are also unanswered questions about long-term trends in sediment yield. It has been suggested that the study of lake sediments may provide a means to monitor the effects of forestry-related activities. However, a better understanding of the connection between catchment disturbance and lake sedimentation is required to assess the suitability of this approach. To explore these questions, lacustrine sedimentary records from three logged basins and one unlogged basin on the west coast of Vancouver Island, British Columbia were analyzed for physical and chemical properties. Core correlations were based on x-radiography and trends in organic content and magnetic susceptibility. Chronological control provided by 210Pb and 137Cs activity demonstrated that 10-30 cm cores record 100-150 years of sediment deposition and allowed the calculation of sediment accumulation rates. Historical information about both natural and human disturbance in the study areas was compared with changes in sediment characteristics and sedimentation rates. Trends in sediment yield and indicator properties associated with disturbance were thereby identified. The results indicate that increases in sediment yield coincide with forestry-related disturbances, natural disturbances such as rainstorm events, and other human activities such as mining. The identification of the sedimentary signature of forestry-related activity is confounded in one of the logged basins by other catchment disturbances. Depositional events identified on the basis of x-ray stratigraphy and sediment properties also coincide with historically documented instances of localized catchment events such as a landslide or forestry-related mass movement in gullies. Of all the sediment properties, changes in the relative proportions of organic and inorganic sediment fractions are most sensitive to upstream catchment conditions as evident from the correspondence between the general change in sediment composition and disturbance history. Methodologically, the results of the study demonstrated that the lake sediment approach may be successful in monitoring the effects of forestry-related activities, although this largely depends on the precision of chronological control, a rigorous sub-sampling strategy and the use of multiple cores. Limitations of the sediment chronology were investigated and show that underestimates of sediment yield may result from assumptions made in the modelling of chronological data. Not all lakes may be suited to this technique due to the resolution required and given that other disturbances occurring in the catchment may obscure inference from the sediment record.

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 score0.035
Threshold uncertainty score0.070

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.006
GPT teacher head0.158
Teacher spread0.152 · 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
Published2009
Admission routes1
Has abstractyes

Explore more

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