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Record W1970308870 · doi:10.1016/j.aaspro.2015.03.032

Soil Hydrodynamic Characteristics of Reclaimed Agricultural Land at Messolonghi's Polder

2015· article· en· W1970308870 on OpenAlexaboutno aff
Nikolaos Malamos, Pantelis Barouchas, Aglaia Liopa-Tsakalidi, Athanasios Koulopoulos, I. Chatziioakeim, Ph. Vitiniotis, Ch. Chalvatzis

Bibliographic record

VenueAgriculture and Agricultural Science Procedia · 2015
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsLand reclamationEnvironmental scienceAgricultural landAgricultureHydrology (agriculture)AgroforestrySoil scienceGeographyGeotechnical engineeringGeologyArchaeology

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate the soil hydrodynamic characteristics, i.e. water retention curve and field saturated hydraulic conductivity, at reclaimed agricultural land of Messolonghi polder in Greece (38o 21’ 58” N; 21o 28’ 41” E). The reclaimed Dutch type pilot-polder of Messolonghi was chosen as study area, because of its characteristics. The area is most likely dedicated to agriculture and was reclaimed from the sea during the decade of 1960-1969. In this context, samples were taken from different locations and depths in an experimental field located in the campus of TEI of Western Greece. The results of the soil texture analysis showed that the selected field's soil is clay loamy - loamy. In order to investigate the water retention curves, undisturbed samples were taken at 30 cm and 60 cm from three points of the field and the analysis was performed with pressure plate apparatus from 0.33 to 6 bar. The field saturated hydraulic conductivity was estimated by Guelph permeameter at 21 points placed in a regular grid for three different depths, i.e. 15-30-60 cm. The results for both parameters were typical but extensive heterogeneity was observed across the field, mainly due to the polder construction procedure.

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 score0.894
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.180
Teacher spread0.174 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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