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Record W2783202736

A Fish Habitat Classification Model for the Upper and Middle Sections of the Bay of Quinte, Lake Ontario

2006· article· en· W2783202736 on OpenAlexfundaboutno aff
Charles K. Minns, Andrea M. Bernard, Carolyn N. Bakelaar, M. Ewaschuk

Bibliographic record

VenueNSUWorks (Nova Southeastern University) · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersFisheries and Oceans Canada
KeywordsBayFish <Actinopterygii>FisheryHabitatFish habitatGeographyOceanographyEcologyGeologyArchaeologyBiology
DOInot available

Abstract

fetched live from OpenAlex

A fish habitat classification model was developed and applied to the upper and middle sections of the Bay of Quinte, Lake Ontario. Available habitat inventories were assembled in a GIS database, bringing bathymetric, shoreline, substrate, and vegetation data together in a series of layers. The classification model was developed in four steps. In the first step, the Defensible Methods (DM) model developed by Minns et al. (2000) was used to estimate suitability values in all habitat patches for a set of nine fish groups each with three life stages. The fish groups were formed from the assemblage of fish species present in the Bay of Quinte by combining them according to thermal and vegetation preferences, and combinations of size and age-at-maturity. Different methods of combining the 27 suitability indices were examined to allow designation of each unique habitat patch to low, medium or high suitability categories for fish. The K-means clustering technique was selected for classifying habitat patches into three suitability categories, thereby exploiting natural breaks in the cumulative distributions of suitability values and maintaining consistency with underlying habitat features. In the second step, the spatially rare habitats for each fish group by life stage combination were used to identify habitat patches that are important for particular fish groups and life stages but which had been classified as medium or low suitability in the first classification step. Criteria for recognizing rarity were used to reassign habitat patches rated low or medium in step one to the high class. In the third step, local expert knowledge of important fish habitats gathered from anglers and fishers were used to develop an expert classification. This expert mapping of important fishing areas was compared with that obtained via suitability and rarity ratings and then, in step four, used to upgrade some areas from low or medium to high. The final habitat classification model is a mixture of suitability, rarity and expert ratings. The habitat suitability class assignments obtained in step one were not changed appreciably by steps two and three. The combined suitability-rarity ratings showed good agreement with the local expert ratings. Important fishing areas either overlapped suitable areas or were close by where fisher access would be restricted by depth or vegetation density. The final habitat classification for the Bay of Quinte provides a context for both conservation and restoration efforts. Periodic updating of the classification system will be needed as conditions change, e.g., as a result of climate change or as the effects of the zebra mussel invasion on macrophytes and substrates mature, or as data on other habitat elements becomes available, e.g., seasonal and spatially thermal habitat maps. Further effort is needed to understand the procedures used by government agencies at different levels to integrate the knowledge embodied in habitat maps into on-going fisheries and fish habitat management.

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 score0.700
Threshold uncertainty score0.305

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 head0.025
GPT teacher head0.184
Teacher spread0.159 · 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

Citations6
Published2006
Admission routes2
Has abstractyes

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