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Record W3063808763 · doi:10.2478/asn-2020-0023

Impact of ecological restoration techniques on the dynamics of degraded ecosystems of the mounts of Saida: Case of the forests of Doui Thabet (West Algeria)

2020· article· en· W3063808763 on OpenAlexaff
Aouadj Sid Ahmed, Yahia Nasrallah, Okkacha Hasnaoui, Hadj Khatir

Bibliographic record

VenueActa Scientifica Naturalis · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsStem Cell Network
Fundersnot available
KeywordsPistacia lentiscusSowingSeedlingStipaTillageBiologyAgronomyBotanyEnvironmental scienceEcologyMediterranean climate

Abstract

fetched live from OpenAlex

Abstract The aim of the present study aims to establish the impact of different restoration techniques (soil and vegetation works) of five pioneer species of the Doui Thabet forest (Mounts of Saida, West of Algeria): Pinus halepensis, Pistacia lentiscus., Tetraclinis articulata, Juniperus oxycedru,. and Stipa capensis (= S. tenacissima L.) between 2018 and 2020. An experimental field device covering an area of 1 ha has been installed in the Doui Thabet forest in a Pinus halepensis massive more than 80 years old. The following work was carried out: mechanical grinding of the vegetation (chopping), turning (scarification of soil) to a depth of 10 cm, tillage (Deep ploughing) to a depth of 20 cm, controlled burning of branches, clearing of Stipa capensis, seedlings planted in different seasons for certain species (Stipa capensis = S. tenacissima L.), monitoring of stump rejections and natural sowing. The sowing result varies according to the species and restoration techniques, it is high for Pinus halepensis and Stipa capensis. It is low for Tetraclinis articulata and Juniperus oxycedrus and none for Pistacia lentiscus while for Stipa capensis it is quite high when planted in autumn compared to spring. Turning and burning have proven to be the most abundant treatments for regeneration and growth. Grinding has medium seedling density and growth, while deep tillage and natural sowing showed low seedling density and growth.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.034
GPT teacher head0.251
Teacher spread0.217 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

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