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Record W6892193676 · doi:10.5167/uzh-120199

Classification of vegetative lagg types and hydrogeomorphic lagg forms in bogs of coastal British Columbia, Canada

2016· article· en· W6892193676 on OpenAlexaboutno aff

Bibliographic record

VenueZurich Open Repository and Archive (University of Zurich) · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionArticular cartilage damageWindageDiafiltrationDemotionExclosure

Abstract

fetched live from OpenAlex

A “lagg” is usually defined as the confined transition zone along the outside margin of a raised bog, characterized by a fen or swamp plant community. It is an important landscape element for biodiversity and helps to maintain a high water table within the peat mass of a bog, but has received little research attention. Greater knowledge of the variability in laggs will improve designation of appropriate conservation sites and restoration of damaged bogs. We therefore examined the hydrological, hydro-chemical, vegetative, and peat characteristics of laggs of bogs in coastal British Columbia, Canada. The 17 studied lagg transects were classified into four vegetative lagg types: Spiraea Thicket, Carex Fen, Peaty Forest, and Direct Transition. These vegetative lagg types fell within two hydrogeomorphic lagg forms: confined or unconfined. The Spiraea Thicket and Carex Fen laggs were topographically confined at the bog margin and characterized by a higher water table and a smaller tree basal area compared to the unconfined Peaty Forest and Direct Transition laggs. Half of the studied laggs were unconfined, highlighting the importance of considering both confined and unconfined laggs in the delineation, conservation, and restoration of raised bog ecosystems.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.165
Teacher spread0.160 · 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
Published2016
Admission routes1
Has abstractyes

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