MétaCan
Menu
← Back to cohort
Record W7067195615

Managing multiple land uses : applications in subarctic Urko Kekkonen National Park, Finland

2004· dissertation· en· W7067195615 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2004
Typedissertation
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationVisitor patternNational parkSubarctic climateTourismLand useLand managementProtected area
DOInot available

Abstract

fetched live from OpenAlex

Evaluating the integration of multiple land uses in protected areas by assessing user satisfaction assists in identifying the balance between ecological protection and the socio-economic and cultural needs of local populations. Urho Kekkonen National Park in northern Lapland, Finland, provides an example of such integration through the management of reindeer herding, visitor recreation and conservation within park boundaries. Through use of questionnaires, discussions, observations and maps, the impacts and perceptions of reindeer herding, visitor recreation and park management upon each other were assessed, including their relation to conservation. Results revealed a complex co-existence of the users, based on the intensity of demand for an area and spatial location within the park, with overall benefits from the existence of the national park. Discussion of similar arctic-subarctic land use issues in Canadian parks management made apparent the many commonalities of the concerns among national parks worldwide.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.002

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 head0.015
GPT teacher head0.261
Teacher spread0.247 · 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 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

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueeScholarship@McGill (McGill)→Same topicNatural Language Processing Techniques→French-language works237,207→