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Record W2111403511 · doi:10.1139/cjfr-31-10-1768

Temporal and spatial variation of nitrogen transformations in nitrogen-saturated soils of a central Appalachian hardwood forest

2001· article· en· W2111403511 on OpenAlexvenueno aff
Frank S. Gilliam, Bradley M. Yurish, Mary Beth Adams

Bibliographic record

VenueCanadian Journal of Forest Research · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
FundersU.S. Forest ServiceU.S. Department of Agriculture
KeywordsNitrogenHardwoodSpatial variabilitySoil waterEnvironmental scienceAppalachian RegionNitrogen cycleForestryAgronomyForest floorSoil scienceEcologyBiologyGeographyMathematicsPhysical geographyChemistry

Abstract

fetched live from OpenAlex

We studied temporal and spatial patterns of soil nitrogen (N) dynamics from 1993 to 1995 in three watersheds of Fernow Experimental Forest, W.V.: WS7 (24-year-old, untreated); WS4 (mature, untreated); and WS3 (24-year-old, treated with (NH4)2SO since 1989 at the rate of 35 kg Nha–1year–1). Net nitrification was 141, 114, and115 kg Nha–1year–1, for WS3, WS4, and WS7, respectively, essentially 100% of net N mineralization for all watersheds. Temporal (seasonal) patterns of nitrification were significantly related to soil moisture and ambient temperaturein untreated watersheds only. Spatial patterns of soil water NO3–of WS4 suggest that microenvironmental variabilitylimits rates of N processing in some areas of this N-saturated watershed, in part by ericaceous species in the herbaceous layer. Spatial patterns of soil water NO3–in treated WS3 suggest that later stages of N saturation may result inhigher concentrations with less spatial variability. Spatial variability in soil N variables was lower in treated WS3 versus untreated watersheds. Nitrogen additions have altered the response of N-processing microbes to environmental factors, becoming less sensitive to seasonal changes in soil moisture and temperature. Biotic processes responsible forregulating N dynamics may be compromised in N-saturated forest ecosystems.

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 score0.742
Threshold uncertainty score0.886

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 science0.0000.000
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 head0.029
GPT teacher head0.255
Teacher spread0.226 · 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

Citations100
Published2001
Admission routes1
Has abstractyes

Explore more

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