<strong>IDENT-MLT</strong> Fall 2020 and spring 2021 <em><strong>O</strong></em><em><strong>rthomosaics</strong></em>
Bibliographic record
Abstract
<b>IDENT-MLT</b> Fall 2020 and Spring 2021 <i><b>O</b></i><i><b>rthomosaics</b></i>Corresponding author: orane.mordacq@gmail.com.Orthomosaic from fall 2020 and spring 2021 for IDENT Montreal (https://treedivnet.ugent.be/ExpIDENT.html) experiment gathered from UAV (Unmanned Aerial Vehicle).<br>Each file is an orthomosaic in TIF format. The name contains the flight number (starting from 1 for each collecting season), the orthomosaic type (either RGB or NIR/RE) followed by the flight date (DDMMYYYY).<br>The RGB and NIR/RE orthomosaics were created using Agisoft software from aerial images of IDENT-Montréal collected in fall 2020 and spring 2021, using a DJI Phantom 4 Pro drone and Sentera sensor. More details about the methodology can be found in Mordacq et al. (unpublished yet) and in the Supplementary Material S3 within (doi: XXXXX).To facilitate data sharing, we have compressed the files to reduce their size (originally around 500 MB each). We used R for compression, employing the RASTER export options "<i>COMPRESS=DEFLATE", "PREDICTOR=2", "ZLEVEL=6</i>", and subsequently reducing the bit depth from 32 to 24.Methods:UAV image acquisition and processing<b> </b>This study aims to investigate the influence of functional composition and competition in a tree's local neighbourhood on leaf phenology. Detailed observations of leaf emergence and senescence were conducted on 12-year-old trees in a B-EF experiment in Montreal, Canada. The autumn 2020 and spring 2021 phenological measurements were achieved using the visual assessment of phenological stages, remote sensing data collection using a UAV, and the Leaf Area Index (LAI), over time. We used a UAV (DJI Phantom 4 Pro, DJI, Shenzhen, China) equipped with a Sentera Double 4K camera (Minneapolis, MN, United-State). Approximately 550 to 600 photos were taken per acquisition date, and these images were used to create geolocated orthomosaic images using R (R Core Team, 2019) and Agisoft Metashape Professional (Agisoft LLC, St. Petersburg, Russia). In the autumn, 62 orthomosaics (~9 mm/px spatial resolution) were generated for 31 dates (mean interval of ~5 days). In the spring, 64 orthomosaics were generated for 32 dates (mean interval of ~4.5 days). Radiometric calibration was done with a MAPIR Camera Reflectance Calibration Ground Target (MAPIR, San Diego, CA, USA). Radiometric corrections, such as exposure compensation and reflectance correction, were done manually using R (Supplementary Material Appendice B). Sensor corrections such as vignetting and lens-related distortion correction were made with Agisoft. To generate georeferenced orthomosaics, 12 ground control points (GCPs) in the form of black and white grid-painted cement blocks were used.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.103 | 0.007 |
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".