Database for Rafieijahed et al. (2025) Canadian Journal of Remote Sensing
Bibliographic record
Abstract
The "Database_Rafieijaed_et_al_2025_Canadian_Journal_of_Remote_Sensing.csv" file contains the database used for the article "Predicting forest age up to 250 years in old-growth boreal mixedwoods of western Quebec, Canada, using airborne laser scanning data and modelled tree species composition" (doi:https://doi.org/10.1080/07038992.2025.2562266) published in the Canadian Journal of Remote Sensing. This database provides airborne LiDAR and tree species modeling predictors for a spatially independent subsample of plots from the datapaper Maleki et al. 2021 "A 249‐yr chronosequence of forest plots from eight successive fires in the Eastern Canada boreal mixedwoods" in Ecology (data doi: http://onlinelibrary.wiley.com/doi/10.1002/ecy.3306/suppinfo), extracted at four different spatial resolutions: 400 m2, 1600 m2, 4900 m2, and 10000 m2. The variables present in the dataset are as follows: RESOLUTION : resolution of the predictors (400 m2, 1600 m2, 4900 m2 or 10000 m2)PLOT_ID : unique plot IDTRANSECT_ID: unique transect IDFOREST_AGE: plot age, here defined as the time since the last fire (years)FIRE_YEAR : year of the last firePERC01: 1st percentile of canopy height returnsPERC05: 5th percentile of canopy height returnsPERC10: 10th percentile of canopy height returnsPERC25: 25th percentile of canopy height returnsPERC75: 75th percentile of canopy height returnsPERC90: 90th percentile of canopy height returnsPERC95: 95th percentile of canopy height returnsPERC99: 99th percentile of canopy height returnsCOEFFICIENT_VARIATION: Coefficient of variation of all height returnsSTANDARD_DEVIATION: Standard deviation of all height returnsMEDIAN: Median of all height returnsSKEWNESS: Skewness of all height returnsPROP_7_12M: Proportion of height returns between 7 and 11.99 mPROP_12_17M: Proportion of height returns between 12 and 16.99 mPROP_17_22M: Proportion of height returns between 17 and 21.99 mPROP_22_27M: Proportion of height returns between 22 and 26.99 mCANOPY_RELIEF_RATIO: [(hmean − hmin)/(hmax − hmin)]GAP_FRACTION: Percentage of returns below 7 mRUGOSITY: Ratio of three-dimensional canopy surface model area to ground areaSYMMETRY_INDEX: [(hmodal - hmean)/(hperc95 - hmin)]GAP_VTMR: Gap variance to mean ratio, based on the method of Zhang et al.(2017) "Characterizing Forest Succession Stages for Wildlife Habitat Assessment Using Multispectral Airborne Imagery" (doi:10.3390/f8070234)MEAN_SLOPE : Mean value of slopeMEAN_TWI : Mean value of topographic wetness index (TWI)SHARE_BETULA_PAPYRIFERA: Mean percentage in modelled merchantable wood volume of Betula papyriferaSHARE_PICEA_GLAUCA: Mean percentage in modelled merchantable wood volume of Picea glaucaSHARE_PICEA_MARIANA: Mean percentage in modelled merchantable wood volume of Picea marianaSHARE_POPULUS_SPP: Mean percentage in modelled merchantable wood volume of Populus spp.SHARE_PINUS_BANKSIANA: Mean percentage in modelled merchantable wood volume of Pinus banksianaSHARE_ABIES_BALSAMEA: Mean percentage in modelled merchantable wood volume of Abies balsameaSHARE_THUJA_OCCIDENTALIS: Mean percentage in modelled merchantable wood volume of Thuja occidentalisSHARE_EARLY_SUCCESSIONAL: Mean percentage in modelled merchantable wood volume of Betula papyrifera, Populus spp. and Pinus banksianaSHARE_PICEA_SPP: Mean percentage in modelled merchantable wood volume of Picea glauca and Picea marianaSHARE_LATE_SUCCESSIONAL: Mean percentage in modelled merchantable wood volume of Abies balsamea and Thuja occidentalis
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.401 | 0.190 |
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".