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Record W7027541420

Conclusion: Land degradation and complex socioecological systems

2019· book-chapter· en· W7027541420 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIRIS Research product catalog (Sapienza University of Rome) · 2019
Typebook-chapter
Languageen
FieldArts and Humanities
TopicReformed Theology and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsLand degradationDesertificationSustainable land managementEnvironmental degradationSoil retrogression and degradationBackwardnessNatural disasterNatural (archaeology)Land managementLand use
DOInot available

Abstract

fetched live from OpenAlex

Ecosystems can be considered as complex systems where different elements (productive, institutional and contextual) act synergistically on environmental conditions and land degradation processes. Soil degradation, drought, poverty, cultural and technological backwardness are the main causes of degradation of both natural and social environments. Such issues usually affect marginal areas, and this happens both in economically-developed countries and in developing regions. In these areas, sustainable land management is recognized as the element on which to act to improve people's living conditions and safeguard the environment (Salvati and Zitti, 2008b; Salvati and Carlucci, 2011, 2014; Salvati et al., 2013a; Zitti et al., 2015; Biasi et al., 2017; Pili et al., 2017). Desertification is the most emblematic case of land degradation, the effects of which were first recognized at the beginning of the 20th century (Kosmas et al., 1999, 2003, 2013; Salvati et al., 2009; Kairis et al., 2013a, 2013b). In 1930, most of the Great Plains of the United States of America suffered a prolonged drought which, together with inappropriate agronomic practices, led to soil degradation, which has gone down in history with the term "dust bowls". Specifically, adverse weather and climate conditions appeared that affected the Central United States and Canada between 1931 and 1939 which, leading to soil deterioration, gave rise to sandstorms. This ecological disaster caused an exodus of more than half a million Americans who left their farms in Texas, Kansas and Oklahoma. Only the adoption of more appropriate cultivation methods and the sustainable management of water resources prevented catastrophic consequences in the event of similar droughts. Unfortunately, this has not remained an isolated episode because adverse climatic conditions and poor land management have led to cases of land degradation in almost all areas worldwide (Moonen et al., 2002; Montanarella, 2007; Salvati et al., 2012a; Colantoni et al., 2015a). Nowadays, global warming, together with the intensification of economic development and population growth, have led to soil degradation, that now affects nearly 40% of the Earth's surface, including some areas of southern Europe (National Committee for the Fight against Desertification, 1998). After experiencing droughts with a general increase in climatic aridity, the Mediterranean basin has in fact been considered one of the most important hotspots for the observation of soil degradation and desertification processes (Kosmas et al., 1999, 2003, 2013; Salvati and Zitti, 2005; Salvati et al., 2009, 2012b; Kairis et al., 2013a, 2013b; Karamesouti et al., 2015; Zambon et al., 2018). It has been widely demonstrated that, in this region, the increasing level of environmental vulnerability is associated with long-term ecological dynamics (e.g., climate aridity, soil deterioration, erosion, salinity and land-use changes) together with socioeconomic, cultural and institutional dynamics that contribute to anthropogenic pressure leading to major landscape transformations (Moonen et al., 2002; Montanarella, 2007; Salvati and Zitti, 2008a; Salvati et al., 2012a; Colantoni et al., 2015a; Di Feliciantonio and Salvati, 2015; Zambon et al., 2017, 2018). All these conditions can be exacerbated by unsustainable land management, especially in fragile areas (Moonen et al., 2002).

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.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.899
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.082
GPT teacher head0.282
Teacher spread0.200 · 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