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Record W2793459572 · doi:10.30960/kjnc.2014.12.1_2.19

Vegetation of the Mt. Seongju

2014· article· en· W2793459572 on OpenAlexaboutno aff
Sang-Kyoo Choi, Jae-Kuk Shim

Bibliographic record

VenueKorean Journal of Nature Conservation · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEcology and Conservation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPinus densifloraQuercus variabilisChamaecyparisVegetation (pathology)ForestryPlant communityBotanyQuercus serrataEcologySpecies diversityPinus koraiensisGeographyBiologySpecies richness

Abstract

fetched live from OpenAlex

The forest vegetation of Mt. Seongju area, near Boryeong, was surveyed from August to October 2013. The vegetation of Mt. Seongju area classified into 13 main plant communities according to the dominant species: Pinus rigida community, Alnus hirsuta community, P. densiflora plantation, Larix leptolepis community, Chamaecyparis obtusa community, Pinus koraiensis community, Quercus variabilis community, Q. variabilis-P. densiflora community, Q. acutissima community, Q. mongolica community, P. densiflora community, P. densiflora - Q. variabilis community, and Carpinus laxiflora community. In addition, the Q. variabilis commintity is distributed on southern slope and Q. mongolica community on the northern slope. Carpinus laxiflora community distributed on the southern slope and ridge of the Mt. Seongju. Another specific communty was P. densiflora plantation, which was artificially planted as the purpose of a restoration after mining several years ago. Q. variabilis community showed higher diversity index than other communities. DGN 7 grade appeared 90.3% of Mt. Seongju area. The frequency distribution in DBH-class of dominant tree species for Q. mongolica community, P. densiflora community, and Carpinus laxiflora community showed stable community structure, but Quercus variabilis community showed intermediate stage of successional processes. The net primary productivity(NPP) was assumed as 1,588.22 g/m2/yr by Miami Model, and 1,488.39 g/m2/yr by Montreal model, respectively.

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)0.000
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 score0.137
Threshold uncertainty score0.095

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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