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Record W4225487996 · doi:10.1071/mf21299

Spatial heterogeneity of the seed bank at a peat lake in Australia

2022· article· en· W4225487996 on OpenAlexaff
Joanne Ling, Wen Li, Ben Ellis, Martin Krogh

Bibliographic record

VenueMarine and Freshwater Research · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsDepartment of Environment and Conservation
Fundersnot available
KeywordsPeatSoil seed bankWetlandVegetation (pathology)Context (archaeology)BiodiversityEnvironmental scienceEcosystemEcologyFlood mythBankHydrology (agriculture)GeographyAgronomyBiologyGerminationGeology

Abstract

fetched live from OpenAlex

Context In the face of global biodiversity decline, understanding the effects of potential climate change on the persistence of soil seed banks is critical, especially in wetland ecosystems. Although studies have explored the response of soil seed banks to changes in periodically inundated wetlands, little is understood about seed banks in peatlands. Aims We examined the spatial variability of soil seed banks during a recent drying event, the last of which occurred over 60 years ago. Methods We sampled the soil seed bank in three zones away from the centre of the dry lakebed at five depth intervals down to 50 cm. Key results Our study showed that the seed bank distribution in a peatland reflected the wetland plants examined at the time of the drying event. The distribution of seeds was along a flood gradient, suggesting an interaction between historical inundation intensity (Zone) and vertical (Depth) distribution of seeds, and correlated with the extant vegetation, as determined during a significant water drawdown period. Conclusions and implications This study shows that the ability of seeds to survive burial, either submerged or desiccated, even after long periods, may prove to have advantages for plant survival and establishment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.006
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.036
GPT teacher head0.283
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 teacher head, not a consensus.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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