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Record W2077547414 · doi:10.1139/x08-004

Role of position within the windthrow in forest floor chemistry in the flysch zone of the Carpathians

2008· article· en· W2077547414 on OpenAlexvenueno aff
Pavel Šamonil, Barbora Šebková, Jan Douda, Tomáš Vrška

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsnot available
FundersMinistry of Environment
KeywordsHumusFlyschBeechForest floorWindthrowSoil horizonChemistryLitterPodzolEnvironmental chemistrySoil waterGeologySoil scienceEcologyGeochemistryBiology

Abstract

fetched live from OpenAlex

A hypothesis was verified that forest floor chemistry varies according to position within the windthrow (mound, pit, or control). A parallel assessment was made of differences in the chemistry of horizons and their forms. A total number of 1720 windthrows were studied in the fir–beech primeval forest in the flysch zone of the Western Carpathians. A total of 100 samples were taken on three positions from the following horizons and their forms (in brackets): litter, fermented (amphigenous, zoogenous, or mycogenous), humification (unresolved, zoogenous, residues, or humic), and mineral. A random sampling eliminated the effect of correlation between the horizons and positions. Parameters assessed were Q4/6, HA/FA, C-forms, total N, P, K, and Mg contents, soil reaction, and cation-exchange capacity. The positions exhibited a significant difference in the forest floor chemistry even at a level of horizon forms. The position’s significance decreased with the horizon depth. Organic horizons in the pit, in particular, exhibited the lowest content of total humic substances, fulvic acids, and the lowest colour coefficient values. However, the mineral horizon showed no significant differences between the positions within the windthrow. Compared with other humus types, the decomposing wood mass did not exhibit a different ratio of humic and fulvic acids.

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)0.000
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 score0.775
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.245
Teacher spread0.216 · 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

Citations21
Published2008
Admission routes1
Has abstractyes

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