Data files for "An assessment of run-of-river hydroelectric dams on mountain stream ecosystems using the American dipper as an avian indicator"
Bibliographic record
Abstract
We monitored American dipper populations over two years at stream sites with and without Run-of-River (RoR) dams across three watersheds in coastal British Columbia, Canada. From September to November of 2014 and 2015, 99 adult dippers (n=48 in 2014 and n=51 in 2015, plus 3 recaptures from 2014) were banded at 13 (7 regulated, 6 unregulated) of the 14 study streams. Blood and feathers were collected during banding for mercury and stable isotope analysis. Streams were surveyed after initial banding attempts in the fall of 2014, during the spring of 2015 (before and after the freshet), and again during the fall of 2015. Density surveys followed the unreconciled independent double-observer approach to target American dippers, whereby each observer keeps a separate tally of all observations to facilitate an estimation of detection probability. We also applied the same method to other aquatic bird species and counted them during each survey. Survey data was used to compare density, seasonal occupancy, and assess differences in year-round residency and philopatry in dippers at regulated and unregulated streams. Dataset for publication "An assessment of run-of-river hydroelectric dams on mountain stream ecosystems using the American dipper as an avian indicator" Ecological Indicators 93 (2018) 942–951. Available at https://doi.org/10.1016/j.ecolind.2018.05.086
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.025 |
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".