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Transport and settlement of organic matter in small streams

2009· article· en· W1996976883 on OpenAlexaff
Trent M. Hoover, Laurie B. Marczak, John S. Richardson, Noboru Yonemitsu

Bibliographic record

VenueFreshwater Biology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSettlingSTREAMSFrondAlderOrganic matterHydrology (agriculture)Environmental scienceGeologyChemistryEcologyBiologyPaleontologyEnvironmental engineeringGeotechnical engineering

Abstract

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Summary 1. After it enters streams, terrestrially derived organic matter (OM) rapidly absorbs water. Using field and laboratory experiments, we examined how this process affected the buoyancy, settling velocity, transport distance and retention locations of four types of organic matter typically found in Pacific coastal streams (‘flexible’ red alder leaves and three ‘stiff’ particle types – Douglas‐fir needles, red cedar fronds and Douglas‐fir branch pieces). 2. Immersion in water rapidly changed the physical characteristics of alder leaves, Douglas‐fir needles and red cedar fronds, which all reached constant still‐water settling velocities after only a few days of soaking. In contrast, the settling velocity of branch pieces continued to increase for 13 days, eventually reaching much higher values than any other OM type. Dried alder leaves became negatively buoyant after only two days of immersion, while other types took substantially longer (up to 24 days) before the specific gravity of all particles was >1. 3. We released saturated OM particles in an experimental channel and found that all particle types travelled further in a fast, shallow ‘riffle’ than a slow, deep ‘pool’. Comparisons with a passive settlement null model indicated that leaves were retained more rapidly than expected in the riffle (by large protruding stones), while the three stiff particle types travelled further than expected (probably due to turbulent suspension) and were retained when they settled in deeper water between larger stones. In pools, passive settlement appeared to dominate the retention of all OM types, with leaves travelling furthest. 4. These retention patterns corresponded well with those observed when saturated OM particles collected in the field were released in two pools and two riffles in a second‐order coastal stream. 5. When the experimental channel and in‐stream data were combined, the retention rates of the three stiff OM types were closely related to calculated Rouse numbers (Rouse number = particle settling velocity/shear velocity), whereas the retention rate of alder leaves was not. This suggests that different physical mechanisms are responsible for the retention of leaves and stiff OM types in shallow streams.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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 teacher head, not a consensus.

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

Citations55
Published2009
Admission routes1
Has abstractyes

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