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Record W4406501917 · doi:10.1002/9781394235230.ch7

Wetlands of Mountainous Region of Bosnia and Herzegovina

2025· other· en· W4406501917 on OpenAlexaboutno aff
Barudanović Senka, Mašić Ermin

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsWetlandOverexploitationDeforestation (computer science)EcosystemGeographyClimate changeHabitatVegetation (pathology)EcologyEnvironmental scienceEnvironmental protection

Abstract

fetched live from OpenAlex

Wetlands (peatlands) of mountainous region of Bosnia and Herzegovina (B&H) represent a relict remain of the vegetation, flora, and fauna from the glaciation periods. This type of ecosystem is widespread in the north, where it occupies large areas of northern Europe, Asia, and Canada. Occurrence of this type of ecosystem in B&H contains an indication of nature conservation in general and represents an extraordinary natural value. The status of peatland ecosystems in the Balkan Peninsula should be carefully monitored, especially today, at the time of the already recognizable effects of climate change. The preserved structure and functionality of these ecosystems might indicate satisfactory degree of resilience to climate change, but adverse state warns of the need to take appropriate actions. Multiple drivers, such as overexploitation of natural resources, water, air, and soil pollution, and the spread of invasive alien species, also have a negative effect on wetlands of mountainous region of B&H. The main identified drivers are deforestation, habitat conversion, and drainage of watercourses. In order to protect this type of ecosystem and important indicator species, it is necessary to implement different conservation and restoration activities.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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
Published2025
Admission routes1
Has abstractyes

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