Repository for : <i>Establishment of reed </i><i>canarygrass</i><i> (</i><i>Phalaris </i><i>arundinacea</i><i>) in the context of floodplain restoration: Impact of companion plant and s</i><i>ow</i><i>ing rate</i><i> </i>
Bibliographic record
Abstract
About this projectThe state of the yellow perch (Perca flavescens) population of the lake Saint Pierre (the largest floodplain in Québec, Canada) is concerning and has been the subject of three moratoria (2012, 2017, 2022). Agriculture intensification in the floodplain in the last century resulted in the loss of important spawning and rearing areas for the yellow perch. Reestablishing perennial vegetation during the spring floods appears essential to ensure the persistence of the population, but the prolonged flooding and fluctuating conditions found in the floodplain make the objective difficult to achieve.In response to this challenge, we evaluate the impact of companion plants (Avena Sativa and Lolium pratense) and sowing rates on the establishment success of Phalaris arundinacea in the floodplain of lake Saint-pierre.DescriptionThis is the online repository to reproduce the results presented in: Mathieu Vaillancourt, Catherine Čapkun-Huot, Samuel Jean Jacques, Bérenger Bourgeois and Monique Poulin. 2024. Establishment of reed canarygrass (Phalaris arundinacea) in the context of floodplain restoration: Impact of companion plant and sowing rate.This repository contains all the data and code for the paper.DataThe data required for this project are separated in two distinct data sets based on the study site:BF.csvSB.csvBF stands for Baie-du-Febvre, and SB stands for Saint-Barthélémy. Data include :Experimental treatments (species sowed and sowing rate)Weed cover and biomass (separated by species and pooled together)P. arundinacea cover, biomass and mean maximum heightThe complete description of these data sets can be found in Metadata_BF.txt and Metadata_SB.txtCodeThe analyses and figures are separated in two .R files:Models.RFigures.R
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.748 | 0.378 |
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".