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Record W4410343968 · doi:10.1007/s13280-025-02171-3

Reconstructing the historical decline of lichen cover across the reindeer fence of the Finnish–Norwegian border

2025· article· en· W4410343968 on OpenAlexaff
Tuomo Wallenius, Jarle W. Bjerke, Rasmus Erlandsson, Tiina H. M. Kolari, Aleksi Räsänen, Teemu Tahvanainen, Hans Tømmervik, Emelie Winquist, Tarmo Virtanen

Bibliographic record

VenueAMBIO · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsUniversité du Québec à Montréal
FundersHelsingin ja Uudenmaan SairaanhoitopiiriHorizon 2020 Framework ProgrammeHelsingin Yliopisto
KeywordsLichenNorwegianGeographyTramplingBiomass (ecology)EcologyHerdingPhysical geographyRangelandForagingGrazingForestryBiology

Abstract

fetched live from OpenAlex

We analysed the history behind the current contrasting lichen covers of two adjacent reindeer herding districts at the Finnish-Norwegian border. We conducted vegetation field inventories across the border fence and reconstructed a lichen cover history from 1959 to 2020 using aerial and satellite images. The oldest images showed only a slight difference in lichen cover between the different sides of the border fence. Since the late 1950s, lichen cover has decreased in both districts. At present, lichen biomass is approximately three times greater in in the Norwegian winter pasture than in the Finnish herding district, which has less strictly defined seasonal pastures. A lichen biomass model indicated that lichen intake by reindeer cannot explain the decline in lichen biomass in either of the districts. We suggest that the lichen decline is mainly due to trampling and foraging-induced loss, while other unknown ecological and climatological factors may also be involved.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.241

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 science0.0010.000
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 head0.012
GPT teacher head0.253
Teacher spread0.241 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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