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Record W2143702019 · doi:10.1080/00288330709509902

Dissolved organic carbon in New Zealand peatlands

2007· article· en· W2143702019 on OpenAlexaff
Tim R. Moore, Beverley R. Clarkson

Bibliographic record

VenueNew Zealand Journal of Marine and Freshwater Research · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcGill University
FundersRoyal Society Te ApārangiRoyal SocietyUniversity of Waikato
KeywordsPeatOmbrotrophicDissolved organic carbonEnvironmental scienceDrainageEnvironmental chemistryHydrology (agriculture)GroundwaterCarbon dioxideTotal organic carbonCarbon fibersBogChemistryGeologyEcologyBiology

Abstract

fetched live from OpenAlex

Abstract We determined the concentration of dissolved organic carbon (DOC) and the specific ultraviolet absorbance (SUVA) of 193 samples of water collected from groundwater, porewater, drainage ditches, and streams at peatlands in New Zealand. There was a wide range in DOC concentration (from 7 to 184 mg litre –1 ), with the smallest concentrations in peatlands where there appeared to be large amounts of iron. Concentrations were large (generally >50 mg litre –1 ) in ombrotrophic (rain‐fed) peatlands and reached the highest values (averaging 81 to 129 mg litre –1 ) in water collected from the Torehape peatland (Waikato region), which is undergoing drainage and harvesting for peat and post‐harvest restoration. These high DOC concentrations suggest that New Zealand peatlands export 10 to 50 g DOC m –2 yr –1 , a significant part of the overall carbon budget of peatlands. Most SUVA measurements ranged from 1.5 to 3.5 litre/mg DOC ‐1 /m –1 ) and suggest that the DOC contains 12 to 25% aromatics.

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)0.000
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 score0.258
Threshold uncertainty score0.512

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.287
Teacher spread0.265 · 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

Citations32
Published2007
Admission routes1
Has abstractyes

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