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Record W2492058774 · doi:10.1017/cbo9781139627085.008

Infectious disease and the conservation of freshwater fish

2015· book-chapter· en· W2492058774 on OpenAlexaff
Martin Krkošek, Robert Poulin

Bibliographic record

VenueCambridge University Press eBooks · 2015
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFreshwater fishFreshwater ecosystemHabitatEcologyHabitat fragmentationBiologyConservation biologyClimate changeGeographyFish <Actinopterygii>EcosystemFishery

Abstract

fetched live from OpenAlex

INTRODUCTION Infectious diseases may be an important component of the conservation of freshwater fish. Rates of infectious diseases in freshwater fish are increasing (Johnson & Paull, 2011), and are likely a consequence of the multiple anthropogenic effects that are making freshwater systems the most degraded ecosystems on Earth (Carpenter et al ., 2011). More broadly, emerging diseases across taxa from all habitats tend to be related to environmental changes such as habitat fragmentation, species translocations/invasions, altered food webs, climate change or pollution (Daszak et al ., 2000; Dobson & Foufopoulos, 2001; Kelly et al ., 2009b). Similar processes may be at play in freshwater systems; however, these systems have received comparatively less attention than their terrestrial and marine counterparts (Johnson & Paull, 2011). However, the role of infectious disease in species endangerment and extinction is complex and debatable (Lafferty & Gerber, 2002), and so it is not immediately clear if increasing rates of disease imply that disease is a threat to the conservation of freshwater fish. The primary reason for this is that transmission efficiency of many pathogens is linked to the density of hosts, and so when hosts become rare, it is expected that diseases will fade out and therefore may not necessarily cause extinction of their host (Grenfell & Dobson, 1995; Hudson et al ., 2001). Indeed, a parasite that eliminates its host population also reduces its own fitness to zero, and so it is not clear if diseases are a direct threat to species persistence. Furthermore, fishes have a common life-history characteristic of relatively high fecundity (egg output) but relatively low survival from egg through to reproductive maturity. Such high mortality within the life cycle is often associated with non-disease related factors such as predation. This leads to potentially complex dynamics (Hatcher et al ., 2012) where different mortality processes may interact in compensatory or synergistic ways. For example, if most juvenile fish will die anyway due to predation, does an increase in infection level correspond to an increase in overall mortality? If predators selectively remove infected prey, this may actually counteract disease mortality and lead to healthier populations or alternatively by making prey easier to capture may increase overall mortality (Packer et al ., 2003; Krkošek et al ., 2011a). Such complex ecological dynamics entangle predator–prey, competition, and host–parasite relationships and can thus lead to multiple mortality processes that may exacerbate or dampen the effects of disease on host populations.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0170.002

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 head0.021
GPT teacher head0.231
Teacher spread0.210 · 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 designNot applicable
Domainnot available
GenreReview

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".

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Citations2
Published2015
Admission routes1
Has abstractyes

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