Establishing highly accurate production-age data using the tree-ring technique of crossdating: a case study for Pacific geoduck (Panopea abrupta)
Bibliographic record
Abstract
We apply the tree-ring technique of crossdating to generate highly accurate age data and evaluate error in annual growth increment (annual growth zone) counts for long-lived Pacific geoduck ( Panopea abrupta ) in the Tree Nob Islands, northern British Columbia, Canada. Crossdating is the most fundamental procedure of tree-ring analysis and is based on the tendency of environmental variability to synchronize the growth of all individuals at a given site. By crossmatching these synchronous growth “bar codes”, all growth increments can be correctly identified and assigned the correct calendar year, including the innermost year of recruitment. In this analysis, a total of 432 geoduck individuals were aged using crossdating methods as well as annual growth increment counts. The entire crossdating process was completed using visual techniques, requiring no additional equipment beyond a microscope or microprojector. When compared with crossdated ages, growth increment counts consistently underaged Pacific geoduck, particularly in the oldest individuals. These inaccuracies obscured major recruitment pulses and underestimated the rarity of strong recruitment events. To date, crossdating has been used to develop growth chronologies in a variety of marine and freshwater bivalve and fish species, but no study has demonstrated how the technique can be used to dramatically and economically improve accuracy in age data.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".