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Record W2137299316 · doi:10.5194/bg-7-4051-2010

Rapid accretion of dissolved organic carbon in the springs of Florida: the most organic-poor natural waters

2010· article· en· W2137299316 on OpenAlexafffund
Carlos M. Duarte, Yves T. Prairie, T. K. Frazer, Mark V. Hoyer, Sky K. Notestein, Rosa Martínez, A. Dorsett, Daniel E. Canfield

Bibliographic record

VenueBiogeosciences · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDissolved organic carbonMacrophyteSpring (device)Environmental scienceHydrology (agriculture)Total organic carbonEnvironmental chemistryGroundwaterChemistryOceanographyGeology

Abstract

fetched live from OpenAlex

Abstract. The concentration of dissolved organic carbon (DOC) in groundwater emanating as spring discharge at several locations in Florida, USA and the net increase in DOC in the downstream receiving waters were measured as part of a larger investigation of carbon dynamics in flowing waters. Springs with high discharge (>2.8 m3 s−1) were found to be the most organic-poor natural waters yet reported (13 ± 1.6 μmol C L−1), while springs with lesser discharge exhibited somewhat higher DOC concentrations (values ranging from 30 to 77 μmol C L−1). DOC concentrations increased rapidly downstream from the point of spring discharge, with the calculated net areal input rate of DOC ranging from 0.04 to 1.64 mol C m−2 d−1 across springs. Rates of DOC increase were generally greater in those springs with high discharge rates. These input rates compare favorably with values reported for gross primary production in these macrophyte-dominated spring systems, assuming that 17% of macrophyte primary production is lost, on average, as DOC. The measures reported here are possible only because of the remarkably low DOC levels in the up-surging groundwaters and the short residency times of the water in the spring-runs themselves.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.068
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.180
Teacher spread0.173 · 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.

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

Citations18
Published2010
Admission routes2
Has abstractyes

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