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Persistence and colonisation as measures of success in bog restoration for aquatic invertebrates: a question of detection

2006· article· en· W2107286411 on OpenAlexaffabout
Marc J. Mazerolle, Monique Poulin

Bibliographic record

VenueFreshwater Biology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité LavalUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsColonisationBogInvertebrateEcologyContext (archaeology)Abundance (ecology)Persistence (discontinuity)PeatBiologyGeographyColonizationGeology

Abstract

fetched live from OpenAlex

Summary 1. van Duinen et al. ( Freshwater Biol. , 2006) raise an interesting point regarding Mazerolle et al. ’s ( Freshwater Biol. , 2006, 51 , p. 333) conclusion on the ability of invertebrates, especially sedentary species, to colonise newly created bogs pools. We wish to clarify that Mazerolle et al. (2006) targeted large arthropods and the absence of smaller sedentary species was purely a result of sampling design. 2. van Duinen et al. (2006) postulate that colonisation rates by bog specialists should be higher in Canada than in the Netherlands, given the extensive amount of intact peatlands in Canada. Here, we emphasise the importance of taking the regional context into account when assessing restoration success as our study site occurs in a landscape where most bog pools have been drained. 3. An evaluation of restoration efforts should focus on both sedentary and vagile invertebrates, to resolve the importance of persistence and colonisation. Such patterns are difficult to interpret, however, when sampling designs and analyses do not account for the probability of detection: an absence may be due to non‐detection or true absence. We strongly urge investigators to directly estimate detection probability in addition to the parameters of interest (e.g. presence, abundance) to provide the best information possible regarding restoration success.

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 score0.112
Threshold uncertainty score0.998

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 science0.0000.000
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 head0.019
GPT teacher head0.240
Teacher spread0.220 · 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

Citations5
Published2006
Admission routes2
Has abstractyes

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