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Record W4401481850 · doi:10.1093/biosci/biae064

Foodscapes for salmon and other mobile consumers in river networks

2024· review· en· W4401481850 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioScience · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
FundersNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsForagingAbundance (ecology)Population growthStewardship (theology)PopulationFish <Actinopterygii>HabitatOptimal foraging theoryGeographyEcologyEnvironmental resource managementFisheryBiologyEnvironmental science

Abstract

fetched live from OpenAlex

Mobile consumers track fluctuating resources across heterogeneous landscapes to grow and survive. In river networks, the abundance and accessibility of food and the energetic consequences of foraging vary among habitats and through time, providing a shifting mosaic of growth opportunities for mobile consumers. However, a framework integrating the spatiotemporal dynamics of growth potential within riverscapes has been lacking. We present the concept of foodscapes to depict the dynamic changes in food abundance, food accessibility, and consumer physiology that contribute to spatial and temporal variation of fish growth in rivers. Drawing on case studies of salmonid fishes from Alaska to California, we illustrate how foodscapes can provide a plethora of foraging, growth, and life history opportunities that potentially contribute to population resilience. We identify knowledge gaps in understanding foodscapes and approaches for stewardship that focus on restoring diverse foraging and growth opportunities for fish and other mobile consumers in river networks.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.931
Threshold uncertainty score0.550

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 studies0.0000.001
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 head0.028
GPT teacher head0.301
Teacher spread0.273 · 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