<strong>IDENT-MLT</strong> Fall 2020 and spring 2021 <em><strong>O</strong></em><em><strong>rthomosaics</strong></em>
Bibliographic record
Abstract
IDENT-MLT Fall 2020 and Spring 2021 OrthomosaicsCorresponding 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). 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). 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 (unpublished yet) and in the Supplementary Material S3 within (doi: XXXXX).To facilitate data sharing, we have compressed the files to reduce their size (originally around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 "COMPRESS=DEFLATE", "PREDICTOR=2", "ZLEVEL=6", and subsequently reducing the bit depth from 32 to 24.Methods:UAV image acquisition and processing 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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.038 |
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".