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Record W6987344626

The Spatial Ecology of Predator-prey Relationships in Lakes

2014· dissertation· en· W6987344626 on OpenAlexfundno aff

Bibliographic record

VenueTSpace (University of Toronto) · 2014
Typedissertation
Languageen
FieldSocial Sciences
TopicRace, Identity, and Education in Brazil
Canadian institutionsnot available
FundersMinistry of Natural Resources
KeywordsPelagic zonePredationFish <Actinopterygii>Water columnForage fishPredatory fishPiscivoreForagingZooplankton
DOInot available

Abstract

fetched live from OpenAlex

The pelagic zone of lakes is defined as the water column over the area of the lake benthos that does not receive enough light from the sun to allow macrophytes to grow. The four chapters of this thesis explore the spatial ecology of predator-prey interactions between schooling fish and their fish predators in this featureless environment. We first developed novel hydroacoustics methods to study fish and fish school swimming behaviour in the pelagic zone (Chapter 1 and 2). Then we characterized our in-situ school formation and prey movement observations using an ideal gas model to better understand the mechanisms that lead to fish and school densities during the daytime (Chapter 2 and 3). With this model we estimated the functional relationship between the schooling prey densities and predator encounter rates, and verified with empirical data a counterintuitive relationship that encounter rates decreased as overall prey densities increased (Chapter 3). The encounter rates suggested that predation within the pelagic zone might be greatly influenced by external forces if they provide spatial structure which encourages greater degrees of prey aggregation in predictable locations. In this regard we examined the predator-prey dynamics under wind and found large redistributions of prey and predators under windy conditions leading to greater aggregations in downwind locations. Further, we found that our study fish were larger in lakes that were oriented into the wind, perhaps demonstrating a benefit to fish growth under windy conditions (Chapter 4).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.293
Teacher spread0.274 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

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