Notice bibliographique
Résumé
The cryosphere (including, snow, glaciers, permafrost, lake and river ice) is an integral element of high mountain regions, which are home to roughly 10% of the global population.Widespread cryosphere changes affect physical, biological and human systems in the mountains and surrounding lowlands, with impacts evident even in the ocean.Building on the IPCC's 5th Assessment Report (AR5), this chapter assesses new evidence on observed recent and projected changes in the mountain cryosphere as well as associated impacts, risks and adaptation measures related to natural and human systems.Impacts in response to climate changes independently of changes in the cryosphere are not assessed in this chapter.Polar mountains are included in Chapter 3, except those in Alaska and adjacent Yukon, Iceland and Scandinavia, which are included in this chapter. Observations of cryospheric changes, impacts, and adaptation in high mountain areasObservations show general decline in low-elevation snow cover (high confidence 1 ), glaciers (very high confidence) and permafrost (high confidence) due to climate change in recent decades.Snow cover duration has declined in nearly all regions, especially at lower elevations, on average by 5 days per decade, with a likely 2 range from 0-10 days per decade.Low elevation snow depth and extent have declined, although year-to-year variation is high.Mass change of glaciers in all mountain regions (excluding the Canadian and Russian Arctic, Svalbard, Greenland and Antarctica) was very likely -490 100 kg m -2 yr -1 (-123 24 Gt yr -1 ) in 2006-2015.Regionally averaged mass budgets were likely most negative (less than -850 kg m -2 yr -1 ) in the southern Andes, Caucasus and the European Alps/Pyrenees, and least negative in High Mountain Asia (-150 110 kg m -2 yr -1 ) but variations within regions are strong.Between 3.6-5.2 million km 2 are underlain by permafrost in the eleven high mountain regions covered in this chapter corresponding to 27-29% of the global permafrost area (medium confidence).Sparse and unevenly distributed measurements show an increase in permafrost temperature (high confidence), for example, by 0.19C 0.05C on average for about 28 locations in the European Alps, Scandinavia, Canada and Asia during the past decade.Other observations reveal decreasing permafrost thickness and loss of ice in the ground.{2.2.2, 2.2.3, 2.2.4}Glacier, snow and permafrost decline has altered the frequency, magnitude and location of most related natural hazards (high confidence).Exposure of people and infrastructure to natural hazards has increased due to growing population, tourism and 1 In this report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium or high.A level of confidence is expressed using five qualifiers: very low, low, medium, high and very high, and typeset in italics, for example, medium confidence.For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.9.2 and Figure 1.4 for more details). 2In this report, the following terms have been used to indicate the assessed likelihood of an outcome or
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».