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Enregistrement W6967728855 · doi:10.5203/0061523

Canadian biodiversity: ecosystem status and trends 2010

2017· dataset· en· W6967728855 sur OpenAlexaboutno aff

Notice bibliographique

RevueUMANCEOS · 2017
Typedataset
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBiodiversityEcosystemHabitatEcosystem servicesWildlifeHabitat destructionRecreationMarine ecosystemStewardship (theology)

Résumé

récupéré en direct d'OpenAlex

Canadian Biodiversity: Ecosystem Status and Trends 2010 is the first assessment of Canada\u2019s biodiversity from an ecosystem perspective. It presents 22 key findings derived from technical background reports. Some findings reveal that much of Canada\u2019s natural endowment remains healthy, including large tracts of undisturbed wilderness, internationally significant wetlands, and thriving estuaries, particularly in sparsely populated or less accessible areas. Forest area is fairly stable. Over half of Canada\u2019s landscape remains intact and relatively free from human infrastructure. Although much is in the more remote North, this also includes large tracts of boreal forest and coastal temperate rainforest. Canada maintains commercial and recreational freshwater and marine fisheries of significant economic and cultural importance. Several stressors that impaired ecosystems in the past have been either removed or reduced. Some marine mammal populations are recovering from past overharvesting. Concentrations of contaminants now phased out of use, such as DDT and PCBs, are declining in wildlife. In the past 15 years, federal, provincial, and territorial terrestrial protected areas have increased in number, area, and diversity of ecosystems represented. Canadians have demonstrated their commitment to biodiversity conservation through the growing number of individuals, groups, and businesses involved in stewardship initiatives. Some key findings highlight areas of concern, where signals suggest that action is needed to maintain functioning ecosystems. These findings include loss of old forests, changes in river flows at critical times of the year, loss of wildlife habitat in agricultural landscapes, declines in certain bird populations, increases in wildfire, and significant shifts in marine, freshwater, and terrestrial food webs. Some contaminants recently detected in the environment are known to be increasing in wildlife. Plant communities and animal populations are responding to climate change. Temperature increases, shifting seasons, and changes in precipitation, ice cover, snowpack, and frozen ground are interacting to alter ecosystems, sometimes in unpredictable ways. Some key findings identify ecosystems in which natural processes are compromised or increased stresses are reaching critical thresholds. Examples include: fish populations that have not recovered despite the removal of fishing pressure; declines in the area and condition of grasslands, where grassland bird populations are dropping sharply; and fragmented forests that place forestdwelling caribou at risk. The dramatic loss of sea ice in the Arctic has many current ecosystem impacts and is expected to trigger declines in ice-associated species such as polar bears. Nutrient loading is on the rise in over 20% of the water bodies sampled, including some of the Great Lakes where, 20 years ago, regulations successfully reduced nutrient inputs. This time, causes are more complex and solutions will likely be more difficult. Lakes affected by acid deposition have been slow to recover, even when acidifying air emissions have been reduced. Invasive non-native species have reached critical levels in the Great Lakes and elsewhere. A strategy of detecting ecosystem change and acting before thresholds are crossed has the greatest likelihood of preventing biodiversity loss. Examples throughout the assessment demonstrate the excellent return on investment from early response and prevention. Restoration, although more costly than prevention, has also had successes. Lessons have been learned from preparing this assessment. Canada\u2019s long-term climate and hydrological monitoring programs ensure the reliability and relevance of climate and water trends in areas where station coverage is good. Equivalent monitoring of biodiversity and ecosystems is rare. Local and regional trends are helpful but usually cannot be extrapolated to a wider scale. Information collected for other purposes is often not useful for understanding changes in biodiversity and ecosystems. Relevant ecosystem-level information is less available than decision-makers may realize. Finally, this assessment would not have been possible without the combined efforts of federal, provincial, and territorial governments in sharing data, knowledge, and perspectives.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,049
Score d'incertitude au seuil0,356

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0060,013
Études des sciences et des technologies0,0030,001
Communication savante0,0040,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0220,006

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.

Tête enseignante Opus0,021
Tête enseignante GPT0,253
Écart entre enseignants0,232 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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