Bibliographic record
Abstract
Lakes are among the most susceptible ecosystems to anthropogenic modification as humans are drawn to freshwaters for the multitude of ecosystems services they provide. The littoral habitat structure of lakes, both in the form of aquatic vegetation (macrophytes) and coarse woody debris (CWD), is often negatively altered by direct and indirect human activities. Little is known about the effect that loss of littoral structure might have on whole lake food webs with respect to energy flow and trophic transfers. The maximum number of trophic transfers within a food web is referred to as food chain length and can control community composition through trophic cascades. Therefore, detectable changes in food chain length likely have impacts on the lake food web. I predicted that loss of littoral structure would result in reduced food chain length due to loss of refuge for intermediate consumers. I used a field study and a simulation model to (1) determine if there was a significant relationship between food chain length and littoral structure in the form of macrophytes and (2) assess the support for two different potential mechanisms (i.e. for changes in refuge or productivity) through which macrophytes and coarse woody debris could alter food chain length. The field study showed that food chain length was positively related to macrophyte abundance. The simulation model shed further insight and showed that refuge provided by macrophytes and CWD may account for observed food chain length increases due to preservation of predatory invertebrates. From these results, we can infer that the loss of littoral habitat structure likely results in a shortening of the lake food chain, which can have implications for community composition, energy flow and whole ecosystem responses to change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".