MétaCan
Menu
Back to cohort
Record W137708448

Session A4 - On the Cutting-Edge: Optimizing Fish Passage Mitigation Decisions in California Watersheds

2012· article· en· W137708448 on OpenAlexaboutno aff
J.R. O’Hanley

Bibliographic record

VenueScholarWorks@UMassAmherst (University of Massachusetts Amherst) · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsnot available
Fundersnot available
KeywordsSession (web analytics)Fish <Actinopterygii>Enhanced Data Rates for GSM EvolutionEnvironmental scienceComputer scienceFisheryHydrology (agriculture)GeologyArtificial intelligenceBiology
DOInot available

Abstract

fetched live from OpenAlex

The California Fish Passage Forum is a consortium of state and federal agencies and NGOs whose mandate is to improve fish passage in anadromous waters. The Forum is now embarking on the implementation of a state wide methodology for prioritizing the removal of artificial fish passage barriers. The methodology, which employs highly sophisticated optimization modeling and solution techniques, represents a radical improvement over standard, scoring-and-ranking type procedures commonly used for prioritizing barriers in the US, Canada and other parts of the world. Optimization based methods provide a systematic and objective means of targeting barrier mitigation decisions which maximize restoration gains given available resources. The optimization methodology being implemented by the Forum integrates information on barrier location, passability and cost together with river habitat and quality data for multiple target species in order to identify cost-efficient passage improvement strategies. Critically, the spatial structure of barriers and the interactive effects of passage improvement on longitudinal connectivity are explicitly taken into consideration. Another key feature of the Forum's prioritization methodology is its ease of use. A user-friendly Windows based program, replete with a graphical user interface, has been implemented, allowing Forum members to quickly and easily generate optimized solutions as well as perform basic what-if analyses in terms of running different budget scenarios and or varying the relative weightings placed on individual target 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.210
Teacher spread0.190 · 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; both teacher heads agree on what is shown here.

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
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueScholarWorks@UMassAmherst (University of Massachusetts Amherst)Same topicEnvironmental Conservation and ManagementFrench-language works237,207