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Record W4393776218 · doi:10.5281/zenodo.3690046

National Forestry Database - Base de données nationales des forêts - Canada

2019· dataset· en· W4393776218 on OpenAlexaffabout
Canadian Council Of Forest Ministers-Conseil Canadien Des Ministres Des Forêts

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsForestryDatabaseGeographyComputer science

Abstract

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le français suit National Forestry Database Overview: The principal role of the National Forestry Database (NFD, http://nfdp.ccfm.org/en/index.php) is to collect and compile national forest data and forest management statistics. The NFD serves as Canada’s credible, accurate, and reliable source of national information on forest management and its impact on the forest resource. Mandated through the Canadian Council of Forest Ministers (CCFM), a partnership composed of fourteen federal, provincial and territorial ministers, the Canadian Forest Service (CFS) at Natural Resources Canada developed and maintains the NFD database and has responsibility for disseminating national forestry statistics. With the guiding support of CCFM members, the CFS collects data from provincial or territorial resource management organisations. Federal land data are provided by the responsible federal departments and compiled by the CFS. Provincial, territorial and federal government partners have a long history of collaboration with sharing data to help demonstrate Canada’s commitment to sustainable forest management. NFD collaborators from 10 provinces, 2 territories and the federal government (Natural Resources Canada's Canadian Forest Service) continue to work toward common objectives: -find a common standard for data collection, compilation, analysis, reporting and dissemination of information and knowledge; -identify priorities for improved data collection, compilation, analysis and reporting; -promote standardization of measurement and terminology, to improve the quality and utility of forestry data; -promote liaison and dialogue with organizations engaged in the collection of forestry data for the purpose of improving the accuracy and efficiency of reporting forestry statistics; and -make information publicly available that provides a comprehensive and objective view of the issues and options faced by the forest sector. Questions specific to local provincial/territorial data and data collection/reporting procedures can be directed to the provincial and territorial governments. Please refer to the National Forestry Database Website for specific contacts (http://nfdp.ccfm.org/en/collaborators.php). The NFD respects Canada’s official languages and is committed to ensuring that all information is available in both English and French. When there is no obligation to provide information in both official languages, content may be available in one official language only, such as in the case of technical documents. ------------------------------------------------------------------------ Aperçu de la base de données nationale sur les forêts: Le rôle principal de la Base de données nationale sur les forêts (BDNF, http://nfdp.ccfm.org/fr/index.php) est de recueillir et de compiler les données forestières nationales et les statistiques de gestion forestière. La BDNF sert de source de données et d’information nationales crédibles, précises, et fiables sur la gestion de la forêt et son impact sur la ressource forestière du Canada. Mandaté par le Conseil canadien des ministres des forêts (CCMF), un partenariat composé de quatorze ministres fédéraux, provinciaux et territoriaux, le Service canadien des forêts (SCF) de Ressources naturelles Canada a élaboré et maintient la base de données et a la responsabilité de diffuser les statistiques forestières nationales. Avec le support des membres du CCMF, le SCF recueille des données auprès des organismes provinciaux ou territoriaux sur leur gestion de ressources forestières. Les données fédérales sont fournies par les ministères fédéraux responsables et sont compilées par le SCF. Nos partenaires des gouvernements provinciaux, territoriaux et fédéral ont une longue histoire de collaboration avec le partage de données pour aider à démonter l’engagement du Canada envers la gestion durable des forêts. Les collaborateurs du BDNF, dix provinces, deux territoires et le gouvernement fédéral (Le Service canadien des forets des Ressources naturelles Canada), continuent de travailler vers des objectifs communs : -trouver une norme commune pour la collecte, la compilation, l'analyse, la communication et la diffusion des informations et des connaissances; -identifier les priorités pour améliorer la collecte, la compilation, l'analyse et la communication des données; - promouvoir la normalisation des mesures et de la terminologie, afin d'améliorer la qualité et l'utilité des données forestières; -promouvoir la liaison et le dialogue avec les organisations engagées dans la collecte de données forestières dans le but d'améliorer l'exactitude et l'efficacité de la communication des statistiques forestières; et -mettre à la disposition du public des informations qui offrent une vue complète et objective des problèmes et des options auxquels le secteur forestier est confronté. Les questions spécifiques aux données locales provinciales / territoriales et aux procédures de collecte / déclaration de données peuvent être adressées aux gouvernements provinciaux et territoriaux. Veuillez consulter le site Web de la base de données nationale sur les forêts pour des contacts spécifiques (http://nfdp.ccfm.org/fr/collaborators.php). La BDNF respecte les langues officielles canadiennes et s'engage à faire en sorte que tous les renseignements soient disponibles en anglais et en français. Lorsqu'il n'y a aucune obligation de fournir des informations dans les deux langues officielles, le contenu peut être disponible dans une seule langue officielle, comme dans le cas des documents techniques.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.086
Threshold uncertainty score0.302

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.042
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0210.039
Science and technology studies0.0060.001
Scholarly communication0.0130.006
Open science0.0050.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0860.068

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 head0.062
GPT teacher head0.224
Teacher spread0.162 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations3
Published2019
Admission routes2
Has abstractyes

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