Bibliographic record
Abstract
These files contain data and scripts used in the analysis for an article titled " Streamwater colour in snow-dominated headwater catchments: natural variability and the effects of forest harvesting," by R.D. Moore, R.D. Winkler and G.D. Hope, to be published in <em>Hydrological Processes</em>. The file <em>upc_water_colour.csv</em> contains the colour data as expressed in true colour units (TCU). The first line is a comment that should be skipped, noting that entries of "creek dry" have been manually edited out of this version of the data. All other editing was performed in the script named <em>0_wrangle_data.r</em>. The columns are as follows: <em>Year</em> - year of observation as four-digit value (e.g., 2005) <em>Date</em> - date as dd-Mmm (e.g., 15-May) <em>Day</em> - day of year (e.g., 1-Jan = 1) <em>Cut241</em> - cumulative area harvested in 241 Creek as a percentage of catchment area <em>Cut242</em> - cumulative area harvested in 241 Creek as a percentage of catchment area <em>wc_240</em> - water colour (TCU) in 240 Creek <em>wc_241</em> - water colour (TCU) in 241 Creek <em>wc_242</em> - water colour (TCU) in 242 Creek The scripts are numbered in the order of dependency. For example, a script beginning <em>0_</em> should be run before running a script beginning <em>1_</em>. The scripts are set up to be run within an R project on the local hard drive. The project directory should contain a folder named <em>data</em> that contains <em>upc_water_colour.csv. </em>All other data sets are accessed programmatically within the scripts. Brief descriptions of the scripts follow: <em>0_wrangle_data.r</em> - Uses functions in the <strong>tidyhydat</strong> package to access streamflow data; corrects some erroneous entries for the water colour data; merges streamflow and colour data sets for further analysis. <em>0_wrangle_spatial_data.r</em> - Accesses digital elevation models (DEMs) catchment boundaries and soil map from the Upper Penticton Creek data repository (zenodo); computes various topographic indices from the DEMS; saves processed files on the local hard drive in a folder named <em>dem</em>, located within the project root folder. <em>1_soil_maps.r </em>- Generates a map of the gleyed soil units (Figure 2). <em>1_q_pca_trimonthly.r </em>- Performs a paired-catchment analysis of the streamflow response to logging using a tri-monthly time step; generates plots of observed and predicted streamflow for 241 and 242 Creeks (Figure 3). <em>1_wc_analysis_post_140.r</em> - Analyses water colour variations and response to logging; generates figures used in the article; analysis focuses on days 145 and on each year due to lack of data for earlier dates in the pre-harvest period. <em>1_catchment_characteristics.r</em> - Computes topographic indices for each catchment and generates a table (Table 1) that contains a summary of catchment characteristics. <em>ch_saga_functions.r</em> - Contains functions that use RSAGA package to process the digital elevation models to remove sinks and calculate contributing area grids.
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 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 | 0.002 | 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 | 0.011 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.132 | 0.001 |
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; both teacher heads agree on what is shown here.
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".