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Record W4312125474 · doi:10.3390/land11122256

Evaluating Water and Carbon Retention in a Low-Order, Designed River Corridor

2022· article· en· W4312125474 on OpenAlexafffundabout
Jaclyn Cockburn, Alex Scott, Paul V. Villard

Bibliographic record

VenueLand · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Guelph
FundersMitacsCanada Foundation for Innovation
KeywordsEnvironmental scienceTributaryHydrology (agriculture)Surface runoffSTREAMSWater resource managementGeographyEcologyGeology

Abstract

fetched live from OpenAlex

As urban residential areas expand into the areas around cities, especially in North America, these areas were previously forested or were converted to agricultural uses (e.g., cropping, grazing). Many of the pre-existing channels were modified prior to residential area expansion and required modification and/or restoration in order for development permits to be granted. These pre-existing channels are often low-order, semi-ephemeral streams with hydrological and geomorphological functions and provide aquatic-terrestrial habitat and ecological linkages. Once restored, these corridors provide important services to the entire river network related to flood-risk mitigation, sediment trapping, and are potential carbon (via particulate organic matter) sinks. This research evaluated water flow and carbon trapping within a low-order tributary of East Morrison Creek in Southern Ontario, Canada in the years immediately following construction. Water level records (5 September and 30 November 2019, and 1 April and 30 November 2020) show that even in its early development this new system was functioning efficiently. Sediment samples taken throughout the 2020 field season determined particulate organic matter was being stored, especially in features where flow was attenuated. Channel roughness imposed by large wood structures promote organic matter deposition within bed sediments and were expected to increase over time. These findings highlight the importance of spatial heterogeneity imposed by the design features used in this reach-scale restoration and serve as a valuable ‘proof of concept’ for future work along the urban-rural interface of expanding cities.

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 score0.057
Threshold uncertainty score0.999

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)0.0020.000

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 head0.018
GPT teacher head0.240
Teacher spread0.222 · 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

Citations3
Published2022
Admission routes3
Has abstractyes

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