MétaCan
Menu
← Back to cohort
Record W6947947140 · doi:10.4224/40003438

On fish in rivers during winter: the challenges posed by ice and a warming climate

2024· report· en· W6947947140 on OpenAlexaffvenueabout

Bibliographic record

VenueNPARC · 2024
Typereport
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsNational Research Council CanadaInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsWater columnSurface waterHydrology (agriculture)Ice formationWater levelFish <Actinopterygii>Lead (geology)Bottom waterSea ice

Abstract

fetched live from OpenAlex

This report is meant to raise general awareness of what is faced by fish in rivers during the winter. As the water temperature drops in the fall, rivers begin to freeze. This occurs at the surface, initially along the shorelines, then away from them. Where the water currents are low enough, a uniform ice cover develops. Higher currents, especially in areas where the water is not deep, will lead to a broken ice cover, which can accumulate in places and form an ice jam. There can also be open water all winter long. Ice can form below the water surface as well, either within the water column or along the riverbed. River ice causes an increase in water levels upstream because of the friction that exists between the flowing water and the bottom surface of the ice cover. A rough bottom surface and thick ice cover can cause significant flooding upstream. The break-up of ice jams may induce a sudden increase in water levels downstream. There are approximately 180 fish species inhabiting Canadian rivers. Some of the most common ones are part of the salmon and trout Family, the minnow and carp Family, and the perch Family. Winter is a difficult time of the year for fish. Depending on geographical location, cold water conditions may last four to eight months. The challenges begin as the water temperature goes down to the freezing point. Fish are cold-blooded animals, and their body temperature is near that of the surrounding water. This causes a reduction in their metabolism. They are then less able to move, and seeking low-energy environments becomes a priority. Ice action is detrimental to fish in a number of ways. It can obstruct the channel and prevent them from seeking a suitable environment, or it can occupy those environments. Underwater ice poses several challenges for fish. For example, if it forms over egg nests, it can inhibit overwinter development of eggs. Surface ice can damage these and other critical habitats if it reaches the river bed or via gouging by drifting pieces. The changes in water levels and water currents during winter are also a liability for fish. Drastic events, such as an ice jam break-up, can have dire consequences for fish. Winter conditions, however, can also favor fish. An ice cover is a solid surface that does not exist in the summer and promotes calm environments when fish need it. It also constitutes a refuge against land-based predators because it is both a visual and a physical barrier. Human activities can directly impact fish ecology – this is briefly reviewed also. The salient concern is the presence of structures built across river channels, such as hydroelectric dams. These are a significant barrier to fish mobility; they are also responsible for modifying the waterway, with a loss in the complexity and natural variability it had before dam construction. The cyclic nature of water releases from hydroelectric dams, which is meant to meet energy demands, is utterly unnatural from an ecological perspective. Human activities can also indirectly impact fish ecology via climate change, primarily through an increase in air temperature, which has an important influence on the formation of ice and river dynamics during winter. In general, a reduction in the length of the ice season is expected, with later freeze-ups and earlier break-ups. Fish ecology will be affected by two concurrent sets of circumstances: water temperatures and ice action. Water temperatures are generally expected to rise, and fish adapted to cool or cold water are vulnerable to climate conditions that lead to warmer waters. Overall, an increase in water temperature will impact the entire food web which the fish depend on. Foreseeing the climate impact on fish in rivers is the subject of ongoing investigations by scientists from several fields of expertise.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.001
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 head0.017
GPT teacher head0.251
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueNPARC→Same topicGenomics and Phylogenetic Studies→French-language works237,207→