Riparian responses to reduced flood flows: comparing and contrasting narrowleaf and broadleaf cottonwoods
Bibliographic record
Abstract
To enable assessment of risks of water management to riparian ecosystems at a regional scale, we developed a quantile-regression model of abundance of broadleaf cottonwoods (Populus deltoides and P. fremontii) as a function of flood flow attenuation. To test whether this model was transferrable to narrowleaf cottonwood (Populus angustifolia), we measured narrowleaf abundance along 39 river reaches in northwestern Colorado, USA. The model performed well for narrowleaf in all 32 reaches where reservoir storage was <75% of mean annual flow. Field data did not fit the model at four of seven reaches where reservoir storage was >90% of mean annual flow. In these four reaches, narrowleaf was abundant despite peak flow attenuation of 45–61%. Poor model performance in these four reaches may be explained in part by a pulse of narrowleaf cottonwood expansion as a response to channel narrowing and in part by differences between narrowleaf and broadleaf cottonwood response to floods and drought. Editor Z.W. Kundzewicz; Guest editor M. AcremanCitation Wilding, T.K., Sanderson, J.S., Merritt, D.M., Rood, S.B., and Poff, N.L., 2014. Riparian responses to reduced flood flows: comparing and contrasting narrowleaf and broadleaf cottonwoods. Hydrological Sciences Journal, 59 (3–4), 605–617.
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 | 0.001 |
| 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) | 0.001 | 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".