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Record W2743948733 · doi:10.19189/map.2016.omb.253

Questioning ten common assumptions about peatlands

2017· article· en· W2743948733 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMires and Peat · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsCentre de Géomatique du Québec
FundersNatural Environment Research CouncilFonds de recherche du Québec – Nature et technologiesHorizon 2020 Framework ProgrammeNature Conservancy of CanadaMitacsMinistry of EnvironmentEuropean CommissionSight Research UKRoyal SocietyEconomic and Social Research CouncilGeological Society of London
KeywordsPeatEnvironmental scienceGeologyGeographyArchaeology

Abstract

fetched live from OpenAlex

Peatlands have been widely studied in terms of their ecohydrology, carbon dynamics, ecosystem services and palaeoenvironmental archives. However, several assumptions are frequently made about peatlands in the academic literature, practitioner reports and the popular media which are either ambiguous or in some cases incorrect. Here we discuss the following ten common assumptions about peatlands: 1. the northern peatland carbon store will shrink under a warming climate; 2. peatlands are fragile ecosystems; 3. wet peatlands have greater rates of net carbon accumulation; 4. different rules apply to tropical peatlands; 5. peat is a single soil type; 6. peatlands behave like sponges; 7. Sphagnum is the main ‘ecosystem engineer’ in peatlands; 8. a single core provides a representative palaeo-archive from a peatland; 9. water-table reconstructions from peatlands provide direct records of past climate change; and 10. restoration of peatlands results in the re-establishment of their carbon sink function. In each case we consider the evidence supporting the assumption and, where appropriate, identify its shortcomings or ways in which it may be misleading.

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.

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 score0.080
Threshold uncertainty score0.818

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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