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Record W7097874845

Jordan and Fanjoy, page 1 Sediment yields and sediment budgets of community water supply watersheds in southeastern British Columbia.

2015· article· en· W7097874845 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsnot available
Fundersnot available
KeywordsSedimentHydrology (agriculture)STREAMSTurbidityErosionWater qualitySedimentary budgetWater supply
DOInot available

Abstract

fetched live from OpenAlex

Abstract: Over the past 10 years, the B.C. Forest Service has measured sediment concentration and turbidity on a number of creeks in the Kootenay region of British Columbia, which are used for community or domestic water supply. This paper summarizes the results of measurements on 11 forested watersheds. Some of them have streamflow stations, so that suspended sediment data collected for water quality purposes can be converted to sediment yield. Reasonable estimates of discharge and yield can be made for the remaining creeks. Both undeveloped and developed watersheds are included. For most watersheds, annual background suspended sediment yields are comparable to or slightly higher than the range of published Water Survey of Canada results for small forested watersheds (about 3 to 10 t/km2/y). These yields are lower than for most watersheds in British Columbia. Streams with low sediment yield have been chosen by communities as water sources because they provide good quality water. In some watersheds with forestry development, sediment yield is significantly greater than background levels, due mainly to erosion from logging roads. However, in most cases, the amount of sediment is still within generally accepted water quality guidelines. In rare cases, landslides caused by forest roads have resulted in very large increases in sediment yield.

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 score0.034
Threshold uncertainty score0.068

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)0.0020.000

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 head0.030
GPT teacher head0.209
Teacher spread0.180 · 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
Published2015
Admission routes1
Has abstractyes

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