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Record W2076834037 · doi:10.5589/m07-050

Current approaches to wetland status and trends monitoring in prairie Canada and the continental United States of America

2007· article· en· W2076834037 on OpenAlexvenueaboutno aff
Thomas E. Dahl, Michael D. Watmough

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsWetlandWildlifeEnvironmental resource managementGeographyResource (disambiguation)Service (business)Scale (ratio)Environmental planningEnvironmental protectionBusinessEcologyEnvironmental scienceCartography

Abstract

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Abstract Canada and the United States share a common concern for the North American wetland resource. Despite their ecological and social importance, comprehensive and scientifically sound data on the national status and trends of Canadian wetlands are lacking. Conversely, in the United States, a nationwide comprehensive inventory and monitoring program providing status and trends information is currently implemented. Canada and the United States recognize that national policy and management questions about wetland resource status rely on scientifically based processes to periodically measure wetland status and trends. Both countries have developed monitoring schemes independently. Program similarities include the selection of a probabilistic sample design and a common definition of wetland loss. Program similarities and the shared concern over the North American wetland resource should act as a catalyst for further cross-border cooperation in the areas of wetlands inventory and monitoring. National wetland monitoring in Canada could likely be accomplished through a program similar to the currently operational US Fish and Wildlife Service (USFWS) and Canadian Wildlife Service (CWS) programs. This paper reviews the operational programs implemented by the USFWS to monitor wetlands at a national scale and the CWS to monitor wetlands in prairie Canada for the purpose of providing suggestions for the development of a national wetlands monitoring program in Canada.Le Canada et les États-Unis partagent une préoccupation commune pour les terres humides de l'Amérique du Nord. Malgré l'importance écologique et sociale de ces dernières, des données exhaustives et scientifiques sur leur état à l'échelle nationale et les tendances font défaut. Aux États-Unis, par contre, on a mis en place un programme complet de surveillance et d'inventaire permettant de fournir de l'information sur l'état et les tendances des terres humides. Le Canada et les États-Unis reconnaissent que les questions portant sur les politiques nationales et la gestion relatives à l'état des terres humides reposent sur des processus scientifiques afin de mesurer, périodiquement, l'état et les tendances des terres humides. Les deux pays ont élaboré, de façon indépendante, des plans de surveillance. Les éléments correspondants des programmes comprennent la sélection d'un plan d'échantillonnage probabiliste et une définition commune de la perte de terres humides. Ces similarités ainsi que l'intérêt partagé pour les terres humides nord-américaines devraient servir de catalyseur pour encourager une coopération transfrontalière en ce qui a trait à l'inventaire et à la surveillance des terres humides. La surveillance des terres humides à l'échelle nationale au Canada pourrait vraisemblablement être réalisée grâce à un programme semblable à ceux de l'United States Fish and Wildlife Service (USFCS) et du Service canadien de la faune (SCF). Le présent document évalue les programmes opérationnels mis en œuvre par l'USFWS pour la surveillance des terres humides à l'échelle nationale, et par le SCF pour la surveillance des terres humides dans les prairies canadiennes, afin de soumettre des propositions pour l'élaboration d'un programme national de surveillance des terres humides du Canada.[Traduit par la Rédaction]

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)0.000
metaresearch head score (Gemma)0.000
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 score0.721
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.206
Teacher spread0.189 · 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

Citations60
Published2007
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Remote SensingSame topicCoastal wetland ecosystem dynamicsFrench-language works237,207