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Record W2355490321

Effects of the planting environment of lawn brick with fly-ash medium on the growth of turfgrass

2003· article· en· W2355490321 on OpenAlexaff
Zheng Hai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsLawnFly ashSowingBrickEnvironmental scienceLolium multiflorumEnvironmental engineeringAgronomyEngineeringWaste managementEcologyCivil engineeringBiology
DOInot available

Abstract

fetched live from OpenAlex

As our environmental awareness strengthened and living standard improved, demand for green parking lots and lawn roadside is enhancing. But the increasing acrea ge of buildings, dams and roads conflicts with the development of lawn in modern cities. In order to settle this contradiction, making full use of lawn bricks b ecomes necessary. Based on the fact that the optimal ratio of fly ash and soil a s lawn medium has been found, this experiment probes into the fertilizing and bi ological effects of lawn bricks with mixed medium of soil and fly ash in their h oles. In order to analyze the brick hole environment, the experiment also desig ns two kinds of contrastive planting environment, one is soil hole environment, the other is mixed environment of soil and fly ash. The soil hole environment has the same holes as lawn bricks, and is infilled by mixed medium of soil and f ly ash. On the surface of the mixed environment of soil and fly ash, four circle s having the same size as the holes in lawn bricks have been lined out. The pot experiment was conducted in March 11 of 2002, which was composed of 3 tr eatments including brick hole environment, soil hole environment and the mixed environment of soil and fly ash. Perennial ryegrass(Lolium multiflorum L. cv, Barmultra) were planted in the holes and circles of different planting env ironment. Each treatment was replicated twenty forth times. The experiment was carried out in the green house, and its purpose was to study and appraise water holding capacity and nutrient supplying capability of the three kinds of plan ting environment, also to investigate their influences on the growth and quality of turfgrass. The results show that, turfgrass tissue under brick hole environment has higher contents of nutrient elements. Its contents of N, K, Na, Cu, Zn are respectivel y 0 073, 0 5517, 0 5263, 4 1287, 1 4044 times higher than those under soil hole environment, and 0 103, 0 3513, 0 5037, 1 695, 0 911 times higher th an those under the mixed environment. Their differences are obvious (P0 05 ). According to the above results, the brick hole environment can benefit plant s greatly. Furthermore, the contents of Fe, Cu and Zn in turfgrass tissue under brick hole environment are far higher than the fitting needs, so it unnecessary to supply iron, copper and zinc fertilizer as usual. It also proves that the br ick hole environment has lowest evaporation rate and highest medium water conte nt. So it has long time water holding capacity to effectively lift the menace of high temperature on turfgrass. In a word, the brick hole environment helps t urf especially the cold season turf go over summertime easily. During the whole growing seasons, the clipping yield of turfgrass under soil hole environment i s higher than that under brick hole environment, but the difference isn't notab le (P 0 05). The clipping yield of turfgrass under mixed environment is lo west. After summertime, the qualities of turfgrass under different planting envi ronment are evaluated. The turf quality under brick hole environment is the bes t and its synthesized score is 3 708, the turf quality under soil hole environ ment, 2 637 scores, the turf quality under the mixed environment, only 2 318 s cores. The diversity of turf grass between the forth and the two later is notabl e (P0 01). Lawn bricks with fly ash medium in their holes used as an environment for lawn growth, can not only increase the acreage of lawn, but also solve the fly ash ou tlet problem.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.004
GPT teacher head0.149
Teacher spread0.145 · 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 designBench or experimental
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
Published2003
Admission routes1
Has abstractyes

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