MétaCan
Menu
Back to cohort
Record W2093416166 · doi:10.1093/biosci/biv009

Geographically Isolated Wetlands are Important Biogeochemical Reactors on the Landscape

2015· article· en· W2093416166 on OpenAlexafffund
John M. Marton, Irena F. Creed, David B. Lewis, Charles R. Lane, N. B. Basu, Matthew J. Cohen, Christopher Craft

Bibliographic record

VenueBioScience · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsWestern University
FundersGlobal Institute for Water Security, University of Saskatchewan
KeywordsBiogeochemical cycleWetlandEcosystemEcosystem servicesEnvironmental scienceWater qualityAquatic ecosystemBiogeochemistryLandscape ecologyBiological integrityClean Water ActEcologyHydrology (agriculture)HabitatBiologyGeology

Abstract

fetched live from OpenAlex

Wetlands provide many ecosystem services, including sediment and carbon retention, nutrient transformation, and water quality improvement. Although all wetlands are biogeochemical hotspots, geographically isolated wetlands (GIWs) receive fewer legal protections compared with other types of wetlands because of their apparent isolation from jurisdictional waters. Here, we consider controls on biogeochemical functions that influence water quality, and estimate changes in ecosystem service delivery that would occur if these landscape features were lost following recent US Supreme Court decisions (i.e., Rapanos, SWANCC). We conclude that, despite their lack of persistent surfacewater connectivity or adjacency to jurisdictional waters, GIWs are integral to biogeochemical processing on the landscape and therefore maintaining the integrity of US waters. Given the likelihood that any GIW contributes to downstream water quality, we suggest that the burden of proof could be shifted to assuming that all GIWs are critical for protecting aquatic systems until proven otherwise.

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)0.002
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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.014
GPT teacher head0.204
Teacher spread0.190 · 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

Citations236
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueBioScienceSame topicSoil and Water Nutrient DynamicsFrench-language works237,207