Ranking the effects of site exposure, plant growth form, water depth, and transparency on aquatic plant biomass
Bibliographic record
Abstract
The maximum depth of macrophyte colonization and depth distribution of macrophyte biomass were assessed over 3 years, in late summer, at six sites in the St. Lawrence River and two sites in the Ottawa River (Lake des Deux Montagnes). Maximum depth of submerged plant colonization could be predicted from the light extinction coefficient (r2 = 0.82) and Secchi disk depth (r2 = 0.80). The aboveground and total biomass of macrophytes were related to a variety of environmental variables as follows in descending order of importance: exposure to wind and waves, plant growth forms, water depth, and light intensity. Together, these variables accounted for 67 and 74% of sampling variability of aboveground and total biomass, respectively. These environmental variables were used to elaborate hierarchical predictive models of aboveground and total biomass of emergent and submerged macrophytes. The empirical relationship that links St. Lawrence River and Ottawa River aquatic plants to environmental variables may eventually allow us to forecast wetland response to changes in water levels and water clarity resulting from climate variability and (or) discharge regulation.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".