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Uncertainty assessment of trophic flows in Hamilton Harbour: A linear inverse modelling analysis

2017· article· en· W6920929448 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Ecological Systems Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTrophic levelEcosystemZooplanktonEcosystem modelInverseFood web

Abstract

fetched live from OpenAlex

Ecopath with Ecosim has been extensively used to examine ecosystem attributes and the effects of management actions. One of the main limitations in using Ecopath to credibly guide management decisions lies in the quality and quantity of the data used. Linear Inverse Modelling treats the problem of ecosystem characterization in a rigorous mathematical way in which the foodweb is described as a (linear) function of the flows and model parameters are (inversely) derived from observed data. In this study, our thesis is that Linear Inverse Modelling can be used as a complement to Ecopath applications to evaluate our confidence in typically reported ecosystem characterizations. Based on a simplified version of a previously published foodweb topology (Hossain et al., 2012), we demonstrate that there is considerable uncertainty associated with the predicted energy flows within the ecosystem of Hamilton Harbour, Lake Ontario, Canada. Uncertainty related to external flows (e.g. respiratory and detrital flows) appears to be much higher than for internal flows associated with predator-prey relationships. Our Linear Inverse Modelling analysis reinforces earlier findings that most of the trophic flows are concentrated within the first two trophic levels, while mass fluxes at the higher trophic levels are significantly lower. The intermediate ecotrophic efficiency for zooplankton suggests that planktivorous fishes do not fully capitalize upon the available food in the system. Our model estimates that a substantial amount of the detrital material is being recycled by the microbial community within the system. Taken together with the significant detrital pool directly supporting zooplankton and oligochaetes/chironomids, this prediction is consistent with recent empirical evidence that particulate organic matter from various allochthonous or autochthonous origins constitute important components of the energy transferred to higher trophic levels. Overall, our Linear Inverse Modelling analysis offers meaningful insights that should contribute towards the development of a reliable ecosystem model for Hamilton Harbour.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.811

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.293
Teacher spread0.247 · 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 designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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