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Record W2884123672 · doi:10.1080/14634988.2018.1497400

Measuring 30 years of improvements to aquatic connectivity in the Greater Toronto Area

2018· article· en· W2884123672 on OpenAlexaffabout
M. Choy, D. D. Lawrie, Christopher B. Edge

Bibliographic record

VenueAquatic Ecosystem Health & Management · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCanadian Forest ServiceNatural Resources CanadaToronto and Region Conservation Authority
Fundersnot available
KeywordsFish migrationTributaryHabitatEnvironmental scienceBiological dispersalSTREAMSAquatic ecosystemEcologyCritical habitatBiological integrityEnvironmental resource managementHydrology (agriculture)GeographyFisheryPopulationComputer scienceBiology

Abstract

fetched live from OpenAlex

Instream barriers (e.g. dams, weirs and road crossings) fragment aquatic habitat and prevent the upstream movement of fish, impairing the ability of fishes to complete critical life stages, access critical habitat and for dispersal among local populations. Mitigation efforts have improved aquatic connectivity to some degree, but it has been challenging to quantify the overall improvement in connectivity without long-term and costly field assessments. The development of spatially explicit habitat connectivity indices make it possible to evaluate current stream connectivity, and quantify the improvement prior mitigation projects have had on connectivity. We combined a list of instream barrier mitigation projects completed in five watersheds in the Toronto (Ontario, Canada) area from 1987–2016 (mitigated barriers) and a previously established inventory of all known instream barriers in 2016 (current barriers). The cumulative improvement to connectivity was measured for potadromous (remain in tributaries) and diadromous (move between tributaries and lake) fish species using the dendritic connectivity index. Aquatic connectivity improved for diadromous species between 0 and 14.5% and for potadromous species between 0.1 and 4.4% in the five studied watersheds. Some variation in improvement among the watersheds can likely be attributed to differences in mitigation strategies among the watersheds and a historical emphasis on mitigating instream barriers to benefit migratory salmonid species.

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)0.002
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 score0.277
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.001

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 head0.030
GPT teacher head0.256
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 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

Citations8
Published2018
Admission routes2
Has abstractyes

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