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Record W2383556815

A Study on the Soil Seed Bank Similarity between Different Dumping Yards and Their Nearby Forests in Jinanqiao Hydropower Station

2012· article· en· W2383556815 on OpenAlexvenueno aff
Jiang Wen-da

Bibliographic record

VenueSeed · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicForest, Soil, and Plant Ecology in China
Canadian institutionsnot available
Fundersnot available
KeywordsYardDumpingSimilarity (geometry)Environmental scienceHydropowerGeographyEcologyBiologyBusinessComputer science
DOInot available

Abstract

fetched live from OpenAlex

Dumping yards generated in Hydropower station construction are one of the most difficult places to restore,to know the relationship of soil seed bank between dumping yards and their nearby forests,a study on the soil seed bank similarity between different dumping yards and their nearby forests in Jinanqiao Hydropower Station was carried out based on seed germination trial.The results showed that the similarity coefficient between Meihe dumping yard platform and its nearby forest was highest of 0.46,the coefficient of 3# dumping yard platform(0.44) was lower than that,and the coefficient of 2# dumping yard platform was lowest of 0.27,it's show a rule that the coefficient descended with the distance and high difference between platform and its nearby forest became longer and larger,as far as the similarity coefficient between platforms and nearby forests are concerned.The similarity coefficient between platforms and nearby forests was larger than that between side-slopes and their nearby forests.Cruciferae and Compositae were dominant families in dumping yards,Cruciferae and Cyperaceae and Compositae were dominant families in their nearby forests,herb were dominant life form in both dumping yards and their nearby forests,17 species among 20 species in the dumping yards are the common species in the nearby forests,the similarity coefficient between the dumping yards and the nearby forests was 0.49.

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 score0.041
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.041
GPT teacher head0.310
Teacher spread0.269 · 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
Published2012
Admission routes1
Has abstractyes

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