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Record W2184232844

The American lobster settlement index at 20 years: looking back - looking ahead

2010· article· en· W2184232844 on OpenAlexaboutno aff
Richard A. Wahle, J. Stanley Cobb, Lewis S. Incze, Peter Lawton, Joanna Gibson, Robert Glenn, Carl Wilson, John Tremblay, Oceans Canada

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsnot available
Fundersnot available
KeywordsAmerican lobsterHomarusFisheryPopulationGeographySettlement (finance)NettingResource (disambiguation)HabitatIndex (typography)EcologyBiologyCrustacean
DOInot available

Abstract

fetched live from OpenAlex

We review the accomplishments and future challenges of larval settlement monitoring for the American lobster (Homarus americanus), following a workshop convened in June 2009 observing the programme’s twentieth anniversary. In the late 1980s the new emphasis on “supply-side ecology” energised researchers to look to larval transport and settlement processes to explain population dynamics of marine species with complex life cycles. At that time, larval settlement indices for spiny lobsters (Panulirus cygnus) in Australia were already demonstrating a capacity to forecast subsequent harvest trends and motivated the search for similar predictive power in the other lobster fisheries. The American Lobster Settlement Index (ALSI) was initiated in 1989 soon after diver-based suction sampling proved an effective way of sampling newly settled lobsters in shallow, cobble-boulder nursery habitats. The survey has expanded from a few sites in coastal Maine, USA, to encompass other lobster-producing regions of the Northeast United States and Atlantic Canada. Supported by state and provincial marine resource agencies, monitoring is conducted annually at the end of the late summer-early autumn postlarval settlement season. The settlement index has been the springboard for numerous research projects, contributing to some 24 peer reviewed publications and technical reports to date. Because of the interest in evaluating the predictive power of the time series for subsequent fishery trends, much research has focused on the pre- and post-settlement processes influencing lobster population dynamics. Owing in part to steep gradients in environmental conditions throughout the species’ range, it is becoming evident ocean-atmospheric processes that influence annual fluctuations in larval supply vary on much larger spatial scales (100-1000 km) than do post-settlement processes, such as predation, disease and intra- and interspecific competition. Thus, unlike the Australian case, forecasting models for the American lobster will likely need to be regionally customised to account for differing regional dynamics. Since 2005, vessel-deployed passive postlarval collectors (plastic coated, wire mesh trays filled with cobbles) have been tested and deployed widely as an alternative to suction sampling to assess lobster settlement in waters where diving is unsafe or impractical. Indeed, collectors may become the tool of choice for settlement monitoring in some regions. As we enter the third decade of the region-wide collaboration, it will be important to continue to assess the value of the index as a tool for stock assessment, forecasting, and a mechanistic understanding of lobster recruitment processes.

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)0.005
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.002

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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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