A visual index for estimating cone production for individual white spruce trees
Bibliographic record
Abstract
The number of cones produced by coniferous trees is commonly estimated by visual counts from the ground of a portion of the tree multiplied by a simple conversion factor. Linear conversion factors have been used to estimate total cone production by white spruce (Picea glauca (Moench) Voss). However, these conversion factors originate from other coniferous species and were often based on assumptions of cone visibility within the crown and not on empirical data. We propose a simple method for estimating the total number of cones produced by individual white spruce. We counted visible cones (an index of cone production, or cone index) on a total of 60 trees located in Alberta and Yukon, Canada, that were then felled and all cones were counted. We found that log(actual total cones) = 0.073 + 1.189 × log(cone index) is more accurate for estimating total cone numbers for white spruce than are other conversion factors (ranging from total cones = 1.5 × cone index to total cones = 3.35 × cone index), as determined using Akaike's information criterion with small sample bias adjustment and a validation data set. The relationship between the index of cone production and actual total cones produced is nonlinear, which is contrary to that proposed for various Pinus species.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".