Uncertainty assessment of trophic flows in Hamilton Harbour: A linear inverse modelling analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.140 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".