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Record W2098490258 · doi:10.1139/x03-148

The influence of decomposing logs on soil biology and nutrient cycling in an old-growth mixed coniferous forest in Oregon, U.S.A.

2003· article· en· W2098490258 on OpenAlexvenueno aff
Julie D.H. Spears, Scott M. Holub, Mark E. Harmon, Kate Lajtha

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
FundersDivision of Environmental BiologyU.S. Department of AgricultureNational Science Foundation
KeywordsSoil waterChronosequenceCoarse woody debrisNutrientForest floorCyclingEnvironmental scienceMineralization (soil science)Organic matterNutrient cycleAgronomyEcologyEnvironmental chemistryBiologyChemistryForestrySoil scienceGeography

Abstract

fetched live from OpenAlex

This study investigated the effect of coarse woody debris (CWD) on mineral soils at the H.J. Andrews Experimental Forest in the central Cascade Range of Oregon, U.S.A. Nutrients in CWD leachates were compared with (i) forest floor (control) leachates, (ii) over a decay chronosequence, and (iii) among CWD of four species. There were few differences among CWD leachates and forest floor leachates. Soils under CWD were warmer but not wetter than control soils. Water-soluble organic carbon was higher in soils under CWD than in controls at 5–15 cm depth (p < 0.02), but soil C concentrations did not differ. Gross N mineralization was faster in control soils. We found no differences in N, P, microbial biomass, Biolog plate assays, or enzyme activity in soils. Nutrient leachate differences among CWD species were small. Differences in solutions and in soils among CWD and controls were largest during the middle decay classes. This study suggests that either (i) CWD has no long-term effect and does not contribute large amounts of organic matter to the soil profile or (ii) the effect of CWD is so prolonged that no spatial affect is noticeable because all soils have been affected by CWD at some time.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.802
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.039
GPT teacher head0.284
Teacher spread0.246 · 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

Citations103
Published2003
Admission routes1
Has abstractyes

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